repo stringlengths 8 116 | tasks stringlengths 8 117 | titles stringlengths 17 302 | dependencies stringlengths 5 372k | readme stringlengths 5 4.26k | __index_level_0__ int64 0 4.36k |
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0bserver07/One-Hundred-Layers-Tiramisu | ['semantic segmentation'] | ['The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation'] | camvid_data_loader.py helper.py fc-densenet-model.py model-dynamic.py model-tiramasu-103.py model-tiramasu-67-func-api.py model-tiramasu-56.py train-tiramisu.py model-tiramasu-67.py load_data Tiramisu normalized one_hot_it Tiramisu Tiramisu Tiramisu Tiramisu Tiramisu step_decay rollaxis print len normalized append rang... | 0bserver07/One-Hundred-Layers-Tiramisu | 0 |
101vinayak/Neural-Style-Transfer | ['style transfer'] | ['A Neural Algorithm of Artistic Style'] | images2gif.py checkImages writeGif get_cKDTree readGif NeuQuant GifWriter intToBin append uint8 astype copy int int checkImages hasattr handleSubRectangles GifWriter writeGifToFile convertImagesToPIL open fromarray asarray seek tell convert append enumerate open | # Neural-Style-Transfer Neural style transfer is an optimization technique used to take two images—a content image and a style reference image (such as an artwork by a famous painter)—and blend them together so the output image looks like the content image, but “painted” in the style of the style reference image. Imple... | 1 |
12kleingordon34/NLP_masters_project | ['word embeddings'] | ['Gender Bias in Contextualized Word Embeddings'] | process_winogender_data.py main process_wino_data process_occ_stats float int list items replace append process_wino_data process_occ_stats | # NLP_masters_project Code base used for NLP project 2020. By Daniel de Vassimon Manela, Boris van Breugel, Tom Fisher, David Errington --- ## Contents * `process_ontonotes.ipynb`: Loads the Ontonotes Release 5.0 data from [Github](https://github.com/yuchenlin/OntoNotes-5.0-NER-BIO.git), and processes raw data into a s... | 2 |
131250208/TPlinker-joint-extraction | ['relation extraction'] | ['TPLinker: Single-stage Joint Extraction of Entities and Relations Through Token Pair Linking'] | tplinker_plus/config.py common/utils.py tplinker/config.py tplinker/train.py tplinker_plus/train.py setup.py common/components.py tplinker/tplinker.py preprocess/__init__.py tplinker_plus/tplinker_plus.py LayerNorm HandshakingKernel DefaultLogger Preprocessor DataMaker4BiLSTM TPLinkerBert HandshakingTaggingScheme DataM... | # TPLinker **TPLinker: Single-stage Joint Extraction of Entities and Relations Through Token Pair Linking** This repository contains all the code of the official implementation for the paper: **[TPLinker: Single-stage Joint Extraction of Entities and Relations Through Token Pair Linking](https://www.aclweb.org/antholog... | 3 |
15saurabh16/Multipoles | ['time series'] | ['Mining Novel Multivariate Relationships in Time Series Data Using Correlation Networks'] | COMET_fMRI_Multiple_Parameters.py COMET_Climate.py bronk_kerb.py empirical_observations.py SignifTesterMultipole.py quick-cliques/utils/edges2snap.py quick-cliques/utils/invertdimacs.py Misc_Modules.py get_graph_in_txt.py COMET_ADVANCED.py RandomSearch_Climate.py COMET_fMRI.py quick-cliques/utils/edge2dimacs.py quick-c... | # Multipoles This repository contains the code that we used to find "multipoles", a new class of multivariate relationship patterns in time series data in datasets from climate and neuroscience domain. See [1],[2] for further details. <b>Instructions to run the code:</b> 1) The code to obtain all the empirical observa... | 4 |
1980x/ABAW2020DMACS | ['facial expression recognition'] | ['Affect Expression Behaviour Analysis in the Wild using Spatio-Channel Attention and Complementary Context Information'] | dataset/affectwild2_dataset.py models/losses.py test_affwild2.py train_affwild2_expw_affectnet.py util.py dataset/sampler.py train_affwild2.py dataset/affectwild2_expw_affectnet.py models/attentionnet.py models/resnet.py validate statistic AverageMeter accuracy save_checkpoint adjust_learning_rate main train val_accura... | # ABAW2020 DMACS SSSIHL <strong>This is code for our submission in the expression track of ABAW 2020 competition.</strong> <strong> Results: https://ibug.doc.ic.ac.uk/resources/fg-2020-competition-affective-behavior-analysis/ </strong> Link to Presentation in FG-2020: https://drive.google.com/file/d/1loxCTklHu5hhkA_3pq... | 5 |
1980x/SCAN-CCI-FER | ['facial expression recognition'] | ['Landmark Guidance Independent Spatio-channel Attention and Complementary Context Information based Facial Expression Recognition', 'Affect Expression Behaviour Analysis in the Wild using Spatio-Channel Attention and Complementary Context Information'] | GACNN/main_sfew.py dataset/ckplus_dataset_cv.py dataset/affectnet_dataset.py main_sfew.py main_oulucasia.py dataset/sampler.py GACNN/main_fplus.py utils/util.py OADN/models/resnet.py dataset/ferplus_dataset.py GACNN/ferplus_dataset.py OADN/train_ferplus.py OADN/dataset/rafdb_dataset_attentionmaps.py OADN/train_sfew.py ... | Our work has been published in Pattern Recognition letters. https://authors.elsevier.com/a/1ca-7cAmylz0f https://www.sciencedirect.com/science/article/abs/pii/S0167865521000489#absh0002 <strong>Title:</strong> Landmark Guidance Independent Spatio-channel Attention and Complementary Context Information based Facial Expr... | 6 |
198808xc/OrganSegC2F | ['pancreas segmentation'] | ['A Fixed-Point Model for Pancreas Segmentation in Abdominal CT Scans'] | OrganSegC2F/init.py OrganSegC2F/DataF.py OrganSegC2F/utils.py OrganSegC2F/fine_surgery.py OrganSegC2F/coarse_training.py OrganSegC2F/coarse_surgery.py OrganSegC2F/oracle_testing.py OrganSegC2F/coarse_fusion.py DATA2NPY/nii2npy.py OrganSegC2F/coarse_testing.py DATA2NPY/dicom2npy.py OrganSegC2F/fine_training.py OrganSegC... | # OrganSegC2F: a coarse-to-fine organ segmentation framework version 1.11 - Dec 3 2017 - by Yuyin Zhou and Lingxi Xie ### Please note: an improved version of OrganSegC2F named OrganSegRSTN is available: https://github.com/198808xc/OrganSegRSTN It outperforms OrganSegC2F by 84.50% vs. 82.37% on the NIH pancreas segmenta... | 7 |
198808xc/OrganSegRSTN | ['pancreas segmentation'] | ['Recurrent Saliency Transformation Network: Incorporating Multi-Stage Visual Cues for Small Organ Segmentation', 'A Fixed-Point Model for Pancreas Segmentation in Abdominal CT Scans'] | OrganSegRSTN/surgery.py OrganSegRSTN/Crop.py OrganSegRSTN/coarse2fine_testing.py OrganSegRSTN/oracle_fusion.py OrganSegRSTN/Uncrop.py OrganSegRSTN/coarse_fusion.py OrganSegRSTN/Crop_old.py OrganSegRSTN/fast_functions.py OrganSegRSTN/indiv_training.py OrganSegRSTN/init.py OrganSegRSTN/coarse_testing.py OrganSegRSTN/Data... | # OrganSegRSTN: an end-to-end coarse-to-fine organ segmentation framework version 2.0 - Jul 31 2018 - by Qihang Yu, Yuyin Zhou and Lingxi Xie ### NOTEs: #### 1. v2.0 is a MAJOR update to v1.0, which we: (1) slightly changed network architecture (score layers are removed and change to a saliency layer), so that networ... | 8 |
1989Ryan/Semantic_SLAM | ['semantic slam', 'semantic segmentation', 'autonomous driving'] | ['Visual Semantic SLAM with Landmarks for Large-Scale Outdoor Environment'] | Third_Part/PSPNet_Keras_tensorflow/train.py Third_Part/PSPNet_Keras_tensorflow/caffe-tensorflow/convert.py Third_Part/PSPNet_Keras_tensorflow/ade20k_labels.py Third_Part/PSPNet_Keras_tensorflow/python_utils/callbacks.py Third_Part/PSPNet_Keras_tensorflow/caffe-tensorflow/examples/imagenet/models/caffenet.py Third_Part/... | # Semantic SLAM  This on-going project is Semantic SLAM using ROS, ORB SLAM and PSPNet101. It will be used in autonomous robotics for semantic understanding and navigation. Now the visualized semantic map with topological information is reachable, wher... | 9 |
1Reinier/Reservoir | ['time series'] | ['Efficient Optimization of Echo State Networks for Time Series Datasets'] | docs/conf.py reservoir/detail/robustgpmodel.py reservoir/esn.py setup.py reservoir/esn_cv.py reservoir/scr.py tests/test_esn.py reservoir/__init__.py reservoir/clustering.py reservoir/detail/esn_bo.py setup_package ClusteringBO EchoStateNetwork EchoStateNetworkCV generate_states_inner_loop SimpleCycleReservoir EchoStat... | Reservoir ========= A Python 3 toolset for creating and optimizing Echo State Networks. >Author: Jacob Reinier Maat, Nikos Gianniotis >License: MIT >2016-2019 Contains: - Vanilla ESN and Simple Cyclic Reservoir architectures. - Bayesian Optimization with optimized routines for Echo State Nets through `GPy`. - Clu... | 10 |
201518018629031/HGATRD | ['graph attention'] | ['Heterogeneous Graph Attention Networks for Early Detection of Rumors on Twitter'] | train.py models.py gat.py layers.py utils.py gcn.py SpGAT GCN GraphConvolution SpGraphAttentionLayer SpecialSpmm SpecialSpmmFunction Model evaluate pass_data_iteratively test adjust_learning_rate train evaluation_4class normalize_adj sparse_mx_to_torch_sparse_tensor load_user_tweet_graph accuracy build_symmetric_adjace... | # HGATRD The implementation of our IJCNN 2020 paper "Heterogeneous Graph Attention Networks for Early Detection of Rumors on Twitter" # Requirements python 3.6.6 numpy==1.17.2 scipy==1.3.1 pytorch==1.1.0 scikit-learn==0.21.3 # How to use ## Dataset | 11 |
21lva/EEN_Tensorflow | ['video prediction'] | ['Prediction Under Uncertainty with Error-Encoding Networks'] | visualize.py new_train_d.py new_models.py new_train_s.py utils.py dataloaders/data_atari.py LatentNetwork DeterministicNetwork validation_epoch train train_epoch validation_epoch train train_epoch read_data log load_model ImageLoader get_batch print epoch_size apply_gradients compute_gradients range compute_gradients e... | # EEN_Tensorflow error encoding network by tensorflow only for breakout. The paper of EEN : https://arxiv.org/abs/1711.04994 | 12 |
21lva/EEN_acrobot | ['video prediction'] | ['Prediction Under Uncertainty with Error-Encoding Networks'] | train_d.py expert_policies/utils/tools.py pa.py models.py predict.py utilsf.py train_s.py changeJson.py LatentNetwork DeterministicNetwork DataMaker load_model DrawGraph invScaling BaseLineModelPredictor mape get_cond LatentModelPredictor validation_epoch train train_epoch validation_epoch train train_epoch read_data l... | ### EEN_acrobot Error encoding network for acrobot error encoding network by tensorflow.keras. The paper of EEN : https://arxiv.org/abs/1711.04994 need: python>=3.5 tensorflow (any version that support tf.keras) numpy matplotlib etc.... | 13 |
3778/Ward2ICU | ['time series'] | ['Real-valued (Medical) Time Series Generation with Recurrent Conditional GANs', 'Ward2ICU: A Vital Signs Dataset of Inpatients from the General Ward'] | tests/test_samplers.py run-experiment.py tests/test_models.py ward2icu/models/classifiers.py ward2icu/metrics.py ward2icu/models/cnngan.py ward2icu/models/__init__.py ward2icu/trainers.py ward2icu/samplers.py tests/test_data.py ward2icu/models/rgan.py ward2icu/utils.py ward2icu/models/rcgan.py tests/test_utils.py tests... | --- <div align="center"> # Ward2ICU [](https://arxiv.org/abs/1910.00752) [](https://research.3778.care/projects/privacy/) [.*
---
This repository contains the source code related to the paper [*"On the Road with 16 Neurons: Mental Imagery with Bio-inspired Deep Neural Networks"*](http://arxiv.org/abs/2003.0874... | 15 |
4kubo/bacf_python | ['visual tracking'] | ['Learning Background-Aware Correlation Filters for Visual Tracking'] | background_aware_correlation_filter.py special_operation/convertor.py utils/arg_parse.py special_operation/resp_newton.py utils/report.py utils/get_sequence.py image_process/feature.py utils/functions.py demo_on_otb.py BackgroundAwareCorrelationFilter show_image_with_bbox get_pixel get_pyhog get_pixels resize_DFT2 resp... | # A port of [BACF in Matlab](http://www.hamedkiani.com/bacf.html) to python 2 Python 2 implementation of Background-Aware Correlation Filters for visual tracking. For more detail, please refer to [arXiv paper](https://arxiv.org/abs/1703.04590) and [the author's website ](http://www.hamedkiani.com/bacf.html) ![tracking_... | 16 |
50kawa/mimick_chainer | ['word embeddings'] | ['Mimicking Word Embeddings using Subword RNNs'] | make_wordlist.py data_make.py util.py eval_wordsimilarity.py main.py model.py ngram zijougosa cos_sim cos_sim load_wordvector mse main calc_batch_loss cos_sim EncoderCNN load_model Interpreter Encoder EncoderGRU EncoderSumFF Model EncoderSum SplitWord Dataread PWIMdata load_word2vec_format load_fasttext_format random_c... | # mimick_chainer https://arxiv.org/abs/1707.06961 の実装をchainerでやってみました。 ## 動かし方 | 17 |
590shun/vsum_dsf | ['video summarization'] | ['Video Summarization using Deep Semantic Features'] | script/summarize.py func/sampling/vsum.py func/dataset/summe.py script/evaluate.py script/uniform_smpl.py func/nets/vid_enc.py func/nets/vid_enc_vgg19.py SUMME Model Model representativeness encodeSeg uniformity VSUM eval_human_summary eval_summary eval_f1 get_flabel uniform_sampling model reduce from_numpy feat sample... | # Video Summarization using Deep Semantic Features これは"Video Summarization using Deep Semantic Features" in ACCV'16 [[arXiv](arxiv.org/abs/1609.08758)]を書き直したものになります。 [実装元のリンク](http://github.com/mayu-ot/vsum_dsf),[備忘録](https://github.com/590shun/paper_challenge/issues/7) ## 実験手順 git clone https://github.com/590shun/v... | 18 |
5gon12eder/msc-graphstudy | ['graph generation', 'data augmentation'] | ['Aesthetic Discrimination of Graph Layouts'] | driver/quarry.py benchmarks/lib/history.py driver/www/property.py driver/www/graph.py utils/prepare-local-diffent-regression.py benchmarks/lib/__init__.py eval/progress.py driver/__init__.py eval/make-config-puncture.py utils/prepare-entropy-regression.py benchmarks/micro-driver.py driver/model.py driver/www/nn.py driv... | <!-- -*- coding:utf-8; mode:markdown; -*- --> <!-- Copyright (C) 2018 Moritz Klammler <moritz.klammler@alumni.kit.edu> --> <!-- --> <!-- Permission is granted to co... | 19 |
7-B/yoco | ['style transfer'] | ['Deep Photo Style Transfer'] | server.py app.py wsgi.py segmentation/torch_neural_style_transfer.py segmentation/merge_image.py convert.py segmentation/evaluate.py main add_header coloring github sketch image_resize convert_to_line png2svg simplify sobel main get_palette get_arguments image_resize image_loader StyleLoss Normalization gram_matrix ims... | <h1 align="center"> <br> <a href="https://141.223.140.22"><img src="img/YOCO-logo.png" alt="YOCO" width="200"><a> <br> </h1> ## Contents - [**Weekly Record**](https://github.com/7-B/yoco/wiki/Development-Record) - [**Reference**](https://github.com/7-B/yoco/wiki/%EC%B0%B8%EA%B3%A0-%EC%9E%90%EB%A3%8C) - [**D... | 20 |
9ruddls3/CRNN_Pytorch | ['optical character recognition', 'scene text recognition'] | ['An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition'] | prepare.py train.py parms.py model/CRNN.py Preparing reset model_train BiDireRNN ToRNN CNN_block CRNN_model str list sort convert Compose tqdm abspath device append to listdir enumerate len hasattr isinstance xavier_normal fill_ Conv2d modules reset_parameters zero_ BatchNorm2d weight Linear train_model join backward p... | # CRNN_Pytorch 이 CRNN 패키지는 제 데이터셋을 기반으로 진행하였고, 구글 코랩을 기반으로 진행하였습니다. 전반적인 Hyper-Parameter는 prams.py 에서 선언되었으며, 이를 수정함으로써 튜닝 할 수 있습니다. 문자열 이미지데이터가 있고, 파일명이 'label.확장자'로 되어있다면, prepare.py 를 통해 Dataloader 로 변환 할 수 있습니다. This package is for my custom Dataset and based on Google Colaborative. If you use this for ... | 21 |
A-Jacobson/Depth_in_The_Wild | ['depth estimation'] | ['Single-Image Depth Perception in the Wild'] | train.py datasets.py models.py train_utils.py criterion.py RelativeDepthLoss NYUDepth HourGlass ConvReluBN InceptionS InceptionL main validate AverageMeter save_checkpoint _fit_epoch prep_img fit NYUDepth load show state_dict plot xlabel ylabel RMSprop parameters save_checkpoint load_state_dict RelativeDepthLoss legend... | # Depth in The Wild Pytorch implementation of Single-Image Depth Perception in the Wild https://arxiv.org/pdf/1604.03901.pdf - [x] Data Loader for NYUDepth - [x] Architecture - [x] Custom Criterion - [x] Train on Small sample - [x] Tweak Architecture - [ ] Fully Train Model (310/~500 epochs) - [ ] Validate Results ## N... | 22 |
A-ZHANG1/PSENet | ['optical character recognition', 'scene text detection', 'curved text detection'] | ['Shape Robust Text Detection with Progressive Scale Expansion Network', 'Shape Robust Text Detection with Progressive Scale Expansion Network'] | util/statistic.py util/tf.py pypse.py util/event.py train_ic15.py models/pvanet.py metrics.py util/feature.py util/proc.py test_ctw1500.py util/rand.py util/t.py util/neighbour.py util/caffe_.py models/__init__.py util/dtype.py dataset/icdar2015_loader.py util/ml.py util/str_.py utils.py util/test.py util/log.py test_i... | # Shape Robust Text Detection with Progressive Scale Expansion Network ## Requirements * Python 2.7 * PyTorch v0.4.1+ * pyclipper * Polygon2 * OpenCV 3.4 (for c++ version pse) * opencv-python 3.4 ## Introduction Progressive Scale Expansion Network (PSENet) is a text detector which is able to well detect the arbitrary-s... | 23 |
AADeLucia/gpt2-narrative-decoding | ['response generation'] | ['Decoding Methods for Neural Narrative Generation'] | data/count_length.py mturk_human_evaluation/fleiss.py mturk_human_evaluation/cohen.py evaluation.py generate_responses.py mturk_human_evaluation/format_generated_narratives_csv.py data/preproc.py mturk_human_evaluation/create_html_survey.py sentBERT distinct_2 distinct_1 clean_response parse_args parse_args batchify pr... | # Decoding Methods for Neural Narrative Generation Alexandra DeLucia\*, Aaron Mueller\*, Xiang "Lisa" Li, João Sedoc This repository contains code for replicating the approach and results of the paper [Decoding Strategies for Neural Narrative Generation](https://arxiv.org/abs/2010.07375). ## Models Our GPT-2 Medium mod... | 24 |
AI-HPC-Research-Team/AIPerf | ['automl'] | ['AIPerf: Automated machine learning as an AI-HPC benchmark'] | examples/trials/network_morphism/cifar10/distributed_utils.py src/sdk/pynni/tests/models/pytorch_models/__init__.py tools/nni_cmd/command_utils.py tools/nni_cmd/url_utils.py tools/nni_annotation/testcase/usercode/nas.py src/nni_manager/training_service/test/mockedTrial.py src/sdk/pynni/tests/test_compressor.py src/sdk/... |    **<font size=4>开发单位：鹏城实验室(PCL)，清华大学(THU)</font>** **<font size=4>特别感谢国防科技大学窦勇老师及其团队的宝贵意见和支持</... | 25 |
AI-secure/VeriGauge | ['autonomous driving'] | ['SoK: Certified Robustness for Deep Neural Networks'] | convex_adversarial/examples/cifar.py recurjac/task_lipschitz.py convex_adversarial/examples/fashion_mnist.py cnn_cert/setup_mnist.py cnn_cert/train_resnet.py cnn_cert/CLEVER/setup_mnist.py recurjac/bound_fastlin_fastlip.py basic/core.py cnn_cert/CLEVER/nlayer_model.py cnn_cert/CLEVER/randsphere.py models/cnn_cert_model... | AI-secure/VeriGauge | 26 |
AIEMMU/MRI_Prostate | ['medical image segmentation', 'medical diagnosis'] | ['Deep learning in magnetic resonance prostate segmentation: A review and a new perspective'] | app/contour.py app/predictor.py app/viewer.py app/hooks/hook-fastprogress.py app/hooks/hook-PySide2.QtWebEngineWidgets.py app/hooks/hook-torchvision.py app/ui.py app/displayView.py app/utils.py app/displayViewModel.py app/dicom.py Contour dicom MainWindow DisplayViewModel predictor get_y enableButtons listify loadConto... | # Automatic Prostate segmentation from MR images This provides some deep Learning code for training a deep learning model for segmentation from Magnetic Resonance(MR) Images. There is also a pyqt software avaialble to allow users to explore the use of the deep learning model that was trained as part of this project. Mo... | 27 |
AIS-Bonn/FreqNet | ['video prediction'] | ['Frequency Domain Transformer Networks for Video Prediction'] | BBall/data_dynamic.py MMNIST/data_dynamic.py show_single_V bounce_n ar bounce_vec bounce_mat sigmoid new_speeds unsigmoid show_A matricize show_V show_single_V bounce_n ar bounce_vec bounce_mat sigmoid new_speeds unsigmoid show_A matricize show_V int norm randn transpose rand dot new_speeds zeros abs array range ar mes... | # Frequency Domain Transformer Networks for Video Prediction If you use the code for your research paper, please cite the following paper: <p> Hafez Farazi<b></b>, and Sven Behnke:<br> <a href="https://arxiv.org/pdf/1903.00271.pdf"><u>Frequency Domain Transformer Networks for Video Prediction</u></a> <a href="... | 28 |
AIS-Bonn/LocDepVideoPrediction | ['video prediction'] | ['Location Dependency in Video Prediction'] | data_dynamic.py show_single_V bounce_n ar bounce_vec bounce_mat sigmoid new_speeds unsigmoid show_A matricize show_V int norm randn transpose rand dot new_speeds zeros abs array range ar meshgrid zeros array range matricize bounce_n array matricize bounce_n array show int sqrt reshape show int print reshape sqrt range ... | # LocDepVideoPrediction If you use the code for your research paper, please cite the following paper: <p> Niloofar Azizi<b>*</b>, Hafez Farazi<b>*</b>, and Sven Behnke:<br> <a href="http://www.ais.uni-bonn.de/~hfarazi/papers/LocDep.pdf"><u>Location Dependency in Video Prediction</u></a> <a href="http://www.... | 29 |
ALBERT-Inc/blog_ssap | ['instance segmentation', 'semantic segmentation'] | ['SSAP: Single-Shot Instance Segmentation With Affinity Pyramid'] | src/loss.py src/graph_partition.py src/SSAP.py src/mydatasets.py greedy_additive calc_js_div Edge make_ins_seg Partition calc_loss l2_loss focal_loss preprocess Mydatasets Up DoubleConv Down OutConv SSAP list heapify contract heappop removed heappush exp clip sum log greedy_additive sorted calc_js_div list ones nodes w... | # SSAP再現実装公開用リポジトリ [SSAP [Proposal-freeなInstance Segmentation手法] の紹介と実験](https://blog.albert2005.co.jp/2020/08/18/ssap/)で使用したプログラムです. ## ファイル構成について ``` ├─ README.md # プロジェクトの説明 ├─ requirements.txt # すべてのPythonプログラムのベースとなるPythonパッケージ ├─ data/ # データフォルダ... | 30 |
AMLab-Amsterdam/CEVAE | ['causal inference'] | ['Causal Effect Inference with Deep Latent-Variable Models'] | cevae_ihdp.py utils.py datasets.py evaluation.py IHDP Evaluator get_y0_y1 fc_net format print write mean range flush | # CEVAE This repository contains the code for the Causal Effect Variational Autoencoder (CEVAE) model as developed at [1]. This code is provided as is and will not be updated / maintained. Sample experiment --- To perform a sample run of CEVAE on 10 replications of the Infant Health and Development Program (IHDP) da... | 31 |
AMLab-Amsterdam/SEVDL_MGP | ['gaussian processes'] | ['Structured and Efficient Variational Deep Learning with Matrix Gaussian Posteriors'] | mnist_classification.py optimizer.py nn_utils.py layers.py yacht_regression.py matrix_layers.py VMGNet.py sample_mgaus sample_gauss sample_mult_noise add_bias kldiv_gamma sample_mgaus2 Layer MatrixGaussDiagLayerLearnP MatrixGaussDiagLayerFF randmat randmat2 randvector tscalar multvector log_f Polygamma change_random_se... | Example implementation of the Bayesian neural network in: ***"Structured and Efficient Variational Deep Learning with Matrix Gaussian Posteriors"***, Christos Louizos & Max Welling, ICML 2016, ([https://arxiv.org/abs/1603.04733]()) This code is provided as is and will not be maintained / updated. | 32 |
ANLGBOY/SoftFlow | ['point cloud generation'] | ['SoftFlow: Probabilistic Framework for Normalizing Flow on Manifolds'] | toy_example/lib/layers/diffeq_layers/__init__.py toy_example/generate2.py toy_example/lib/layers/diffeq_layers/basic.py pointclouds/metrics/pytorch_structural_losses/nn_distance.py toy_example/lib/layers/container.py toy_example/lib/layers/odefunc.py pointclouds/utils.py pointclouds/metrics/pytorch_structural_losses/se... | # SoftFlow: Probabilistic Framework for Normalizing Flow on Manifolds This repository provides the implementation of SoftFlow on toy dataset and point clouds. Move to each folder, follow the instructions and enjoy the results! ## Overview <p align="center"> <img src="assets/training_technique.png" height=256/> </p>... | 33 |
ANLGBOY/WaveNODE | ['speech synthesis'] | ['WaveNODE: A Continuous Normalizing Flow for Speech Synthesis'] | preprocessing.py train.py data.py odefunc.py hps.py args.py layers.py synthesize.py test_speed.py test_cll.py utils.py model.py parse_args _pad collate_fn_synthesize _pad_2d collate_fn LJspeechDataset Hyperparameters fused_add_tanh_sigmoid_multiply ActNorm CNF WaveNetPrior logabs MovingBatchNorm1d MBNLayer remove NODEB... | # Pytorch Implementation of WaveNODE PyTorch implementation of WaveNODE ## Abstract In recent years, various flow-based generative models have been proposed to generate highfidelity waveforms in real-time. However, these models require either a well-trained teacher network or a number of flow steps making them memory-i... | 34 |
APooladian/ProxLogBarrierAttack | ['adversarial attack'] | ['A principled approach for generating adversarial images under non-smooth dissimilarity metrics'] | mnist-example/model/utils.py proxlogbarrier_Top1.py mnist-example/model/__init__.py proxlogbarrier_Top5.py mnist-example/model/mnist.py InitMethods.py prox.py mnist-example/run_attack.py mnist-example/model/blocks.py simplex.py GaussianInitialize UniformInitialize BlurInitialize HeatSmoothing SafetyInitialize L2NormPro... | # ProxLogBarrierAttack Public repository for the ProxLogBarrier attack, described in [A principled approach for generating adversarial images under non-smooth dissimilarity metrics](https://arxiv.org/abs/1908.01667). Abstract: Deep neural networks perform well on real world data, but are prone to adversarial perturbati... | 35 |
ARMargolis/melanoma-pytorch | ['network pruning'] | ['The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks'] | model/data.py scripts/train_model.py model/model.py MelanomaDataset MelanomaNet BasicBlock BottleneckBlock | Goal: A PyTorch model for https://www.kaggle.com/c/siim-isic-melanoma-classification/data Technique: Pruning https://pytorch.org/tutorials/intermediate/pruning_tutorial.html#iterative-pruning Theory: Lottery Ticket Hypothesis https://arxiv.org/pdf/1803.03635.pdf # Setup ## Colab stuff To use the kaggle API in CoLab, fo... | 36 |
ASMIftekhar/VSGNet | ['human object interaction detection'] | ['VSGNet: Spatial Attention Network for Detecting Human Object Interactions Using Graph Convolutions'] | scripts_hico/HICO_eval/sample_complexity_analysis.py scripts/calculate_ap_classwise.py scripts_hico/train_test.py scripts/dataloader_vcoco.py scripts/prior_vcoco.py scripts_hico/helpers_preprocess.py scripts_hico/calculate_map_vcoco.py scripts_hico/model.py scripts_hico/HICO_eval/bbox_utils.py scripts/pred_vis.py scrip... | # VSGNet ### [**VSGNet:Spatial Attention Network for Detecting Human Object Interactions Using Graph Convolutions**](http://openaccess.thecvf.com/content_CVPR_2020/papers/Ulutan_VSGNet_Spatial_Attention_Network_for_Detecting_Human_Object_Interactions_Using_CVPR_2020_paper.pdf) [Oytun Ulutan*](https://sites.google.com... | 37 |
AWLyrics/SeqGAN_Poem | ['text generation'] | ['SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient'] | lyric_preprocessing.py preprocessing.py bleu_calc.py dataloader.py mydis.py myG_beta.py rhyme.py seq_gan.py data/preprocess.py mygen.py id2poem.py session_save.py translate.py calc_bleu Input_Data_loader Gen_Data_loader Dis_dataloader load_data linear highway Discriminator Generator G_beta rhyme2 rhyme1 generate_sample... | # SeqGAN Poem 修改 SeqGAN 用于诗歌生成（去除 Oracle LSTM） ## Pipeline - 收集唐诗预处理（preprocessing.py），唐诗[数据集](https://github.com/chinese-poetry/chinese-poetry)，选取了 5000 首五言律诗（20 词） - Tokenize，将中文字转换为 index（train.txt），建立词表（dict.pkl），记录 vocab size - Pretrain Generation - Adersarial Training ## Reference Paper: https://arxiv.org/abs/160... | 38 |
AWehenkel/DAG-NF | ['density estimation'] | ['Graphical Normalizing Flows'] | UCIExperiments.py ImageExperimentsTest.py models/Conditionners/DAGConditioner.py UCIdatasets/miniboone.py ToyExperiments.py models/Normalizers/Normalizer.py UCIdatasets/__init__.py models/NormalizingFlowFactories.py models/__init__.py UCIdatasets/gas.py lib/toy_data.py models/Conditionners/CouplingConditioner.py UCIdat... | # Graphical Normalizing Flows Offical codes and experiments for the paper: > Graphical Normalizing Flows, Antoine Wehenkel and Gilles Louppe. (May 2020). > [[arxiv]](https://arxiv.org/abs/2006.02548) # Dependencies The list of dependencies can be found in requirements.txt text file and installed with the following c... | 39 |
AaltoVision/MaskMVS | ['depth estimation'] | ['Unstructured Multi-View Depth Estimation Using Mask-Based Multiplane Representation'] | models/__init__.py models/MaskNet.py models/DispNet.py generate_volume.py generate_volume gen_mask_gt where warping_neighbor DispNet upconv predict_disp conv crop_like mask_layer down_conv_layer up_conv_layer MaskNet crop_like conv_layer append size enumerate warping_neighbor bmm view grid_sample Variable clamp size ex... | # MaskMVS Yuxin Hou · [Arno Solin](http://arno.solin.fi) · [Juho Kannala](https://users.aalto.fi/~kannalj1/) Codes for the paper: * Yuxin Hou, Arno Solin, and Juho Kannala (2019). **Unstructured multi-view depth estimation using mask-based multiplane representation**. *Scandinavian Conference on Image Analysis (SCIA)*.... | 40 |
AaltoVision/Object-Retrieval | ['image retrieval'] | ['Context Aware Query Image Representation for Particular Object Retrieval'] | SA.py | # Object-Retrieval Particular object retrieval using CNN Code for the arxiv submission https://arxiv.org/pdf/1703.01226.pdf. The source code is a modification of the codes available at http://www.xrce.xerox.com/Our-Research/Computer-Vision/Learning-Visual-Representations/Deep-Image-Retrieval. Follow the instructions i... | 41 |
Abe404/segmentation_of_roots_in_soil_with_unet | ['semantic segmentation'] | ['Segmentation of Roots in Soil with U-Net'] | src/frangi/test.py src/data_utils.py src/frangi/segment.py src/unet/train.py src/unet/sys_utils.py src/unet/im_utils.py src/unet/elastic.py src/unet/log.py src/unet/unet.py src/metrics.py src/unet/checkpointer.py src/unet/loss.py src/unet/test.py src/frangi/train.py src/frangi/cmaes_utils.py src/unet/datasets.py src/fr... | Abe404/segmentation_of_roots_in_soil_with_unet | 42 |
AbertayMachineLearningGroup/machine-learning-SIEM-water-infrastructure | ['anomaly detection', 'cyber attack detection'] | ['Improving SIEM for Critical SCADA Water Infrastructures Using Machine Learning'] | preprocessing.py classification-with-confidence.py classification.py ResultData calculate_accuracy write_data_to_file main main main read_file_and_write_rows DataSetFile count_nonzero index argsort ResultData predict_proba classes_ unique zeros range len StratifiedKFold SVC KNeighborsClassifier values LogisticRegressio... | # Improving SIEM for Critical SCADA Water Infrastructures Using Machine Learning This work aims at using different machine learning techniques in detecting anomalies (including hardware failures, sabotage and cyber-attacks) in SCADA water infrastructure. ## Dataset Used The dataset used is published [here](https://www.... | 43 |
AbnerHqC/GaitSet | ['gait recognition'] | ['GaitSet: Regarding Gait as a Set for Cross-View Gait Recognition', 'GaitSet: Cross-view Gait Recognition through Utilizing Gait as a Deep Set'] | train.py model/network/basic_blocks.py model/utils/sampler.py model/network/__init__.py model/network/gaitset.py model/model.py model/utils/__init__.py work/OUMVLP_network/basic_blocks.py test.py model/utils/data_loader.py work/OUMVLP_network/gaitset.py model/initialization.py config.py model/network/triplet.py model/_... | # GaitSet [-blue.svg)](https://github.com/996icu/996.ICU/blob/master/LICENSE) [](https://996.icu) GaitSet is a **flexible**, **effective** and **fast** network for cross-v... | 44 |
ActiveVisionLab/ANCNet | ['semantic correspondence'] | ['Correspondence Networks with Adaptive Neighbourhood Consensus'] | lib/model.py lib/tools.py lib/pf_dataset.py lib/normalization.py lib/point_tnf.py lib/visualisation.py lib/pf_pascal_dataset.py lib/constant.py lib/conv4d.py lib/im_pair_dataset.py lib/torch_util.py lib/interpolator.py lib/plot.py lib/transformation.py lib/dataloader.py lib/eval_util.py eval_pf_pascal.py main Conv4d co... | ActiveVisionLab/ANCNet | 45 |
AdamByerly/micro-pcb-analysis | ['data augmentation'] | ['On the Importance of Capturing a Sufficient Diversity of Perspective for the Classification of micro-PCBs'] | train.py constructs/loggable.py etc/create_tf_records.py constructs/learning_rate.py input/simple_input_pipeline.py models/model_base.py models/nn_ops.py models/variable.py constructs/metrics.py input/micro_pcb_input_pipeline.py input/micro_pcb_input_pipeline2.py constructs/optimizer.py models/SimpleMonolithic.py input... | # Documentation is forthcoming... | 46 |
AdamCobb/GP-LAPLACE | ['gaussian processes'] | ['Identifying Sources and Sinks in the Presence of Multiple Agents with Gaussian Process Vector Calculus'] | code/GP_deriv.py code/utils.py code/GP_vec.py notebooks/nb_utils.py code/MAS_exp.py code/utils_synthetic.py Pred_var_deriv Pred_mean Loglik optNLLfun SE_covariance_derivative_x1x2 DerivGP var_bounds SE_covariance_derivative_x1 SE_covariance_derivative_xx Pred_var DerivGP_2nd SE_covariance_derivative_x2 SE_covariance Pr... | # GP-LAPLACE A Gaussian process based technique for locating attractors from trajectories in time-varying fields. This repository contains code used in the experiments of our paper: "Identifying Sources and Sinks in the Presence of Multiple Agents with Gaussian Process Vector Calculus" by Adam D. Cobb, Richard Everett,... | 47 |
Adamdad/Filter-Gradient-Decent | ['stochastic optimization'] | ['Stochastic Gradient Variance Reduction by Solving a Filtering Problem'] | optmizor/sgd_WT.py model/MLP.py model/CIFAR10/Resnet.py model/MNIST/Resnet.py optmizor/sgd.py utils/ploter.py optmizor/sgd_MA.py MNIST_exp.py plot.py numberical_plot.py CIFAR10_exp.py optmizor/sgd_ARMA.py NUMBERICAL_exp.py optmizor/Kalman_opt.py optmizor/__init__.py model/NonConvex.py main train accuracy test main trai... | # Filter-Gradient-Decent Update: This project also include the code for paper **Kalman Optimizer for Consistent Gradient Descent** *Xingyi Yang, (ICASSP2021)* [paper](https://ieeexplore.ieee.org/document/9414588) Course project for ECE 251C UCSD. Code for paper, **Stochastic Gradient Variance Reduction by Solving a Fil... | 48 |
AdeDZY/DeepCT | ['passage retrieval'] | ['Context-Aware Sentence/Passage Term Importance Estimation For First Stage Retrieval'] | scripts/get_training_query_term_recall_1to1.py modeling.py optimization.py optimization_test.py tokenization_test.py run_deepct.py scripts/bert_term_sample_to_json.py scripts/bert_term_sample_to_json_car.py to_sentences.py modeling_test.py tokenization.py HDCT/passage2doc_bert_term_sample_to_json.py scripts/get_trainin... | # DeepCT and HDCT: Context-Aware Term Importance Estimation For First Stage Retrieval This repository contains code for two of our papers: - arXiv paper "Context-Aware Sentence/Passage Term Importance Estimation For First Stage Retrieval" [arXiv](https://arxiv.org/abs/1910.10687), 2019 - The WebConf2020 paper "Context... | 49 |
Adelaide-AI-Group/MCVL | ['visual localization', 'visual place recognition'] | ['Visual Localization Under Appearance Change: Filtering Approaches'] | libs/vlfeat-0.9.21/docsrc/doxytag.py libs/vlfeat-0.9.21/docsrc/mdoc.py libs/vlfeat-0.9.21/docsrc/wikidoc.py libs/vlfeat-0.9.21/docsrc/webdoc.py libs/vlfeat-0.9.21/docsrc/formatter.py Doxytag Terminal Lexer B PL L lex Formatter DL BL E extract towiki depth_first breadCrumb MFile Node runcmd xscan wikidoc usage bullet in... | # MCVL Visual Localization Under Appearance Change: A Filtering Approach(DICTA 2019 Best paper) https://arxiv.org/abs/1811.08063 About ============ MATLAB code of our DICTA 2019 paper: "Visual Localization Under Appearance Change: A Filtering Approach" - DICTA 2019 **(Best paper award)**. [Anh-Dzung Doan](https://sites... | 50 |
Adelaide-AI-Group/ST-CLSTM | ['depth estimation', 'monocular depth estimation'] | ['Exploiting temporal consistency for real-time video depth estimation'] | CLSTM_Depth_Estimation-master/prediction/utils_for_2DCNN_prediction/functions_for_prediction.py CLSTM_Depth_Estimation-master/models_CLTSM/R_NLCRNN_modules.py CLSTM_Depth_Estimation-master/models_2D/backbone_dict.py CLSTM_Depth_Estimation-master/models_discriminator/resnet_models.py CLSTM_Depth_Estimation-master/demo/d... | # ST-CLSTM Exploiting temporal consistency for real-time video depth estimation (ICCV 2019) https://arxiv.org/abs/1908.03706 By Haokui Zhang, [Chunhua Shen](https://cs.adelaide.edu.au/~chhshen/), Ying Li, Yuanzhouhan Cao, [Yu Liu](https://sites.google.com/site/yuliuunilau/home), Youliang Yan Some video results can be f... | 51 |
Adirlou/OptML_Project | ['stochastic optimization'] | ['Decentralized Stochastic Optimization and Gossip Algorithms with Compressed Communication'] | decentralized_SGD_logistic.py decentralized_SGD_classifier.py print_util.py quantizer.py decentralized_SGD_least_squares.py communicator.py helpers.py Communicator DecentralizedSGDClassifier DecentralizedSGDLogistic plot_losses standardize plot_losses_with_std run_logistic load_data load_csv_data run_logistic_n_times c... | # Optimization for Machine Learning: Mini-Project ## Convergence of Decentralized SGD under Various Topologies *by Paul Griesser, Adrien Vandenbroucque, Robin Zbinden* In this project, we propose Python code to compute a decentralized version of the SGD algorithm presented initially in the Github repository [here](http... | 52 |
Aditi138/LASE-Agreement | ['cross lingual transfer'] | ['Automatic Extraction of Rules Governing Morphological Agreement'] | annotation_site/serve.py create_trees_website.py dataloader.py create_triples.py utils.py computeAverage.py baseline.py automated_metric addValuesEval printTreeWithExamplesPDF getLeafInfo printPOSInfomation train getTree FrequretrivePossibleTuples getUnique retrivePossibleTuples DataLoader wasserstein_distance colorRet... | # LASE: Automated Extraction of Agreement Rules ## Requirements A python version >=3.5 is required. Additional requirements are present in the **requirements.txt** 1. In the **decision_tree_files.txt**, enter the path to the treebanks for which you want to extract the rules. This code can work with UD/SUD dependency (v... | 53 |
AdityaGolatkar/Sparse-Kernel-PCA-for-outlier-detection | ['outlier detection'] | ['Sparse Kernel PCA for Outlier Detection'] | skpca_codes/kspca_fashion_g2.py skpca_codes/kspca_cancer_g2.py skpca_codes/kspca_fashion_g.py skpca_codes/kspca_fruits2_var.py skpca_codes/kspca_digit_g.py skpca_codes/kspca_digit_g2.py skpca_codes/kspca_cancer_g.py skpca_codes/kspca_fruits.py skpca_codes/kspca_fruits2.py skpca_codes/kspca_satimage2_g.py skpca_codes/ks... | # Sparse-Kernel-PCA-for-outlier-detection Codes and results for our work on SKPCA for outlier detection. Paper link : https://arxiv.org/abs/1809.02497. | 54 |
AdivarekarBhumit/ID-Card-Segmentation | ['face detection'] | ['MIDV-500: A Dataset for Identity Documents Analysis and Recognition on Mobile Devices in Video Stream'] | test_model.py model/iou_loss.py model/train.py model/unet_model.py dataset/download_dataset.py dataset/stack_npy.py main read_image main IoU v_generator t_generator get_model load COLOR_BGR2GRAY fillPoly reshape shape resize zeros imread array cvtColor open str remove sorted replace print glob system rmtree save zip ap... | # ID-Card-Segmentation Segmentation of ID Cards using U-Net ### U-Net Architecture <img src="http://deeplearning.net/tutorial/_images/unet.jpg" width="500" height="400" alt="U-net"> ### Our Result's   - There are five benchmark datasets tested on the proposed methodology: 1) Berlin Halenseestrasse 2) Berlin Kudamm 3) Berlin A100 4) Garden Point 5) Syhthesized Nordland ... | 57 |
AidanRocke/vertex_prediction | ['semantic segmentation'] | ['Annotating Object Instances with a Polygon-RNN'] | vertex_pred/image_encoder.py vertex_pred/training_image_encoder.py vertex_pred/group_norm.py vertex_pred/test.py GroupNormalization image_encoder MyTest encoder_inference files_to_array training_batch reset_default_graph image_encoder zeros load append files_to_array arange choice | AidanRocke/vertex_prediction | 58 |
Aiqz/bayes-by-hypernet | ['normalising flows'] | ['Implicit Weight Uncertainty in Neural Networks'] | run_map_exp.py run_bbb_cifar_resnet_exp.py run_bbb_exp_kernels.py run_bbb_exp.py run_dropout_cifar_resnet_exp.py layers.py utils.py run_mnf_cifar_resnet_exp.py run_mnf_exp.py run_ensemble_cifar_resnet_exp.py base_layers.py experiments.py run_dropout_exp.py networks.py run_bbh_cifar_resnet_exp.py run_bbh_exp.py experime... | Aiqz/bayes-by-hypernet | 59 |
AirBernard/Scene-Text-Detection-with-SPCNET | ['scene text detection', 'instance segmentation', 'semantic segmentation'] | ['Scene Text Detection with Supervised Pyramid Context Network'] | train.py data/__init__.py demo.py nets/config.py nets/utils.py nets/resnet_v1.py nets/model.py nets/resnet_utils.py data/icdar.py data/data_util.py get_model_list get_image_list InferenceConfig read_image get_result main GeneratorEnqueuer get_image build_rpn_targets generator get_batch polygon_area resize_image_and_ann... | # Scene-Text-Detection-with-SPCNET
Unofficial repository for [Scene Text Detection with Supervised Pyramid Context Network][https://arxiv.org/abs/1811.08605] with tensorflow.
## 参考代码
网络实现主要借鉴Keras版本的[Mask-RCNN](https://github.com/matterport/Mask_RCNN.git),训练数据接口参考了[argman/EAST](https://github.com/argman/EAST).论文作者在知... | 60 |
Aitslab/BioNLP | ['multi label classification'] | ['Macro F1 and Macro F1'] | jennie_jesper/evaluation.py hannes/keras_model/train-silver-standard.py formatconversion/format_conversion_scripts/BioXML_IOB2_conversion_tool.py olof_vilhelm/keywords.py antton/formatting/gold_to_text.py jennie_jesper/tagger.py emil_petter/evalCombined.py Adam_Ola/Format_Input/formatInputFile.py jennie_jesper/random_s... | BioNLP ======= Repository for student projects within biomedical text mining from Lund University. All code comes with a GPLv3 licence. # Resources ## NLP libraries AllenNLP NLP library built on PyTorch https://allennlp.org/ CoreNLP contains many of Stanford’s NLP tools | 61 |
Akanni96/feng-hirst-rst-parser | ['discourse segmentation'] | ['Two-pass Discourse Segmentation with Pairing and Global Features'] | src/parse2.py src/parser_wrapper2.py src/test_feng.py src/imdb_script.py src/trees/lexicalized_tree.py src/classifiers/crf_classifier.py tools/crfsuite/crfsuite-0.12/swig/python/setup.py src/utils/serialize.py src/utils/treebank_parser.py tools/crfsuite/crfsuite-0.12/example/chunking.py src/document/dependency.py src/d... | ## DEVELOPERS * Original author: [Vanessa Wei Feng](mailto:weifeng@cs.toronto.edu), Department of Computer Science, University of Toronto, Canada * [Arne Neumann](mailto:github+spam.or.ham@arne.cl) updated it to use nltk 3.4 on [this github repo](https://github.com/arne-cl/feng-hirst-rst-parser), and created a Docker... | 62 |
AkiraTOSEI/Funnel-Activation-for-Visual-Recognition | ['scene generation', 'semantic segmentation'] | ['Funnel Activation for Visual Recognition'] | train_test.py resnet.py FReLU.py FReLU Identity_basic_block Conv_bottleneck_block define_Pooling define_GlobalPooling Fin_layer Identity_bottleneck_block define_ConvLayer define_activation ResnetBuilder define_NormLayers Conv_basic_block Conv_stage1_block image_shift flip_image bn1 pool1 define_Pooling ConvLayer define... | # Funnel-Activation-for-Visual-Recognition this repository is for check FReLU([arXiv:2007.11824](https://arxiv.org/abs/2007.11824)) on CIFAR10. I have tested ReLU, Swish and FReLU 3times using ResNet18. The result is shown as following.  ![alt text](https://raw.githubusercontent.com/AlbanSeurat/keras-style... | 64 |
AlbertChen1991/nEM | ['relation extraction', 'denoising'] | ['Uncover the Ground-Truth Relations in Distant Supervision: A Neural Expectation-Maximization Framework'] | main.py data_loader.py model.py DataLoader RE_Dataset train_epoch_RE get_pre_recall em_step pred_RE test_model train_EM MAP e_step PR_curve save_checkpoint resume pr_with_threshold train_RE precision_recall main write_list_to_file use_optimizer PCNNEncoder CNNEncoder Embedding Extractor BagEncoder Y_S GRUEncoder RE Z_Y... | # nEM Code and data for EMNLP2019 Paper ["Uncover the Ground-Truth Relations in Distant Supervision: A Neural Expectation-Maximization Framework"](https://arxiv.org/pdf/1909.05448.pdf). ## Description This work focuses on the noisy-label problem in distant supervision, while most of the previous works in this setting a... | 65 |
AlbertUW807/DLNN-Algo | ['stochastic optimization'] | ['Adam: A Method for Stochastic Optimization'] | Optimization/test_cases.py Logistic Regression/Logistic_Regression.py Model Initialization/init_utils.py Regularization Methods/reg_utils.py Deep Learning Model/helper_functions.py Model Initialization/initialization.py Optimization/opt_utils.py Gradient Check/gc_utils.py Gradient Check/test_cases.py Optimization/optim... | # DLNN-Algo 〽️ Deep Learning & Neural Networks Projects 〽️ ### Install Numpy ``` $ install numpy ``` ### Projects #### [Logistic Regression](https://github.com/AlbertUW807/DLNN/tree/master/Logistic%20Regression) - Implemented an Image Recognition Algorithm that recognizes cats with 67% accuracy! - Used a logistic ... | 66 |
AleT-Cig/DependencySyntax_DeepIrony | ['word embeddings'] | ['Multilingual Irony Detection with Dependency Syntax and Neural Models'] | Tweet.py Features_manager.py Model_udpipe.py Main_Generate_Output.py Database_manager.py Database_manager make_database_manager make_feature_manager Features_manager Model_udpipe Tweet strip_accents make_tweet Database_manager Features_manager Tweet encode normalize decode | # **Multilingual Irony Detection with Dependency Syntax and Neural Models** Code repository for COLING 2020 submission. Further details will be added upon acceptance and when the anonymity period will be over. | 67 |
AleksandarHaber/Subspace-Identification-State-Space-System-Identification-of-Dynamical-Systems-and-Time-Series- | ['time series analysis', 'time series'] | ['Subspace Identification of Temperature Dynamics'] | functionsSID.py discretization_test.py test_subspace.py simulate whiteTest modelError systemSimulate_Kopen estimateInitial estimateInitial_K systemSimulate_Kclosed portmanteau estimateMarkovParameters estimateModel systemSimulate zeros range pinv zeros range matmul svd concatenate matmul sqrt pinv zeros range diag zero... | AleksandarHaber/Subspace-Identification-State-Space-System-Identification-of-Dynamical-Systems-and-Time-Series- | 68 |
AlessandroSaviolo/HBPSegmentation | ['semantic segmentation'] | ['Learning to Segment Human Body Parts with Synthetically Trained Deep Convolutional Networks'] | utils.py predict.py preprocessing_module.py main getArgs predict save HEDnetwork detect save run getDataLoader parseHistory getAugmentation toTensor normalize getPreprocessing Dataset visualize getDataLoader visualize glob makedirs len astype ValidEpoch DiceLoss save info range run axis close add_axes shape imshow save... | # Human Body Part Segmentation This repository contains the code associated to our paper: *Learning to Segment Human Body Parts with Synthetically Trained Deep Convolutional Networks*. <p align="center"> <img src="https://github.com/AlessandroSaviolo/HBPSegmentation/blob/main/paper/framework.png" width="800"> <... | 69 |
Alex-Fabbri/lang2logic-PyTorch | ['semantic parsing'] | ['Language to Logical Form with Neural Attention'] | seq2seq/atis/lstm/tree.py seq2tree/atis/lstm/data.py seq2seq/jobqueries/attention/util.py seq2tree/geoqueries/attention/util.py seq2tree/geoqueries/lstm/tree.py seq2seq/geoqueries/lstm/sample.py seq2seq/jobqueries/lstm/util.py seq2tree/atis/lstm/tree.py seq2tree/jobqueries/lstm/main.py seq2seq/jobqueries/attention/main... | This repo contains a PyTorch port of the lua code [here](https://github.com/donglixp/lang2logic#setup) for the paper ["Language to Logical Form with Neural Attention."](https://arxiv.org/pdf/1601.01280.pdf) This code was written last year as part of a project with [Jack Koch](https://jbkjr.com) and is not being activel... | 70 |
AlexMeinke/certified-certain-uncertainty | ['out of distribution detection'] | ["Towards neural networks that provably know when they don't know"] | utils/traintest.py utils/models.py utils/eval.py utils/confident_classifier.py run_training.py utils/mc_dropout.py utils/preproc.py utils/deep_ensemble.py utils/dataloaders.py utils/adversarial.py gen_attack_stats.py utils/odin.py model_paths.py utils/single_maha.py utils/gmm_helpers.py utils/hendrycks.py load_pretrain... | # Towards neural networks that provably know when they don't know This repository contains the code that was used to obtain the results reported in https://arxiv.org/abs/1909.12180. In it we propose a *Certified Certain Uncertainty* (CCU) model with which one can train deep neural networks that provably make low-confid... | 71 |
AlexMoreo/TweetSentQuant | ['sentiment analysis'] | ['Tweet Sentiment Quantification: An Experimental Re-Evaluation'] | src/quapy/util.py src/quapy/__init__.py src/tables.py src/quapy/classification/svmperf.py src/quapy/method/aggregative.py src/settings.py src/quapy/functional.py src/quapy/method/base.py src/quapy/method/non_aggregative.py src/main.py src/quapy/dataset/text.py repair_semeval15_test.py src/app_helper.py src/quapy/error.... | # Tweet Sentiment Quantification: An Experimental Re-Evaluation ## ECIR2021: Reproducibility track This repo contains the code to reproduce all experiments discussed in the paper entitled _Tweet Sentiment Quantification: An Experimental Re-Evaluation_ which is submitted for consideration to the _ECIR2021's track on Re... | 72 |
AlexandreAbraham/frontiers2013 | ['time series'] | ['Machine Learning for Neuroimaging with Scikit-Learn'] | scripts/miyawaki_decoding.py scripts/visualization_101.py scripts/adhd_ica.py scripts/miyawaki_encoding.py scripts/utils/datasets.py scripts/haxby_decoding.py scripts/utils/masking.py scripts/utils/resampling.py scripts/utils/searchlight.py scripts/adhd_clustering.py scripts/utils/signal.py plot_labels plot_ica_map plo... | Machine Learning for Neuroimaging with Scikit-Learning ======================================================= Paper on using scikit-learn for NeuroImaging, for the special issue "Python in Neurosciences II" of frontiers in NeuroInformatics. The scripts that can generate the figure, and underly the examples of the pape... | 73 |
AlexeySorokin/NeuralMorphemeSegmentation | ['morphological analysis'] | ['Convolutional neural networks for low-resource morpheme segmentation: baseline or state-of-the-art?'] | neural_morph_segm.py data/morphochallenge_to_morphemes.py tabled_trie.py read.py Partitioner is_correct_morpheme_sequence get_next_morpheme_types load_cls get_next_morpheme generate_data make_model_file read_config _make_vocabulary measure_quality to_one_hot collect_buckets make_bucket_lengths generate_BMES partition_t... | AlexeySorokin/NeuralMorphemeSegmentation | 74 |
AliLotfi92/Deep-Variational-Information-Bottlenck | ['adversarial attack'] | ['Deep Variational Information Bottleneck'] | VIBV4.py weights evaluate_test mulitlayer_perceptron bias truncated_normal constant NormalWithSoftplusScale relu weights matmul add bias histogram sample run | # Deep Variational Information Bottleneck This repository provides the implementation of Deep Variational Information Bottleneck. The main idea of DVIB is to impose a bottleneck (here in the dimensionality) through which only necessary information for the reconstruction of $X$ can pass. I tried to implement this in the... | 75 |
AliLotfi92/Deep_Variational_Information_Bottlenck | ['adversarial attack'] | ['Deep Variational Information Bottleneck'] | VIBV4.py weights evaluate_test mulitlayer_perceptron bias truncated_normal constant NormalWithSoftplusScale relu weights matmul add bias histogram sample run | # Deep Variational Information Bottleneck This repository provides the implementation of Deep Variational Information Bottleneck. The main idea of DVIB is to impose a bottleneck (here in the dimensionality) through which only necessary information for the reconstruction of $X$ can pass. I tried to implement this in the... | 76 |
AliLotfi92/SNNLSTM | ['time series'] | ['Long Short-Term Memory Spiking Networks and Their Applications'] | WordTOVec.py SpikingFSDD.py SpikingMNIST.py WordLevelSpikingLSTM.py CharLevelSpikingLSTM.py SpikingEMNIST.py LSTM_Cell deriv_Tanhspike LSTM_Sample softmax deriv_spike spike cross_entropy LSTM_Cell deriv_Tanhspike predict Test softmax deriv_spike spike cross_entropy LSTM_Cell predict deriv_spike2 Test softmax deriv_spik... | # Spiking LSTM [Long Short-Term Memory Spiking Networks and Their Applications](https://dl.acm.org/doi/abs/10.1145/3407197.3407211) in Python  ### Requirements - Python 3.7 ### How to run ```bash python SpikingEMINST.py ``` # Results | 77 |
AliOsm/semantic-question-similarity | ['question similarity', 'data augmentation'] | ['Tha3aroon at NSURL-2019 Task 8: Semantic Question Similarity in Arabic'] | extract_sentences_embeddings.py 1_preprocess.py plot_sequence_weighted_attention.py 4_train.py data_generator.py plot_sentences_embeddings.py plot_examples_per_data_augmentation_type.py 5_infer.py vote.py 3_build_embeddings_dict.py 2_enlarge.py helpers.py add_item dfs build_model DataGenerator f1 process map_sentence l... | [](https://paperswithcode.com/sota/question-similarity-on-q2q-arabic-benchmark?p=tha3aroon-at-nsurl-2019-task-8-semantic) # Semantic-Question-Similarity The... | 78 |
Alibaba-NLP/DAAT-CWS | ['chinese word segmentation'] | ['Coupling Distant Annotation and Adversarial Training for Cross-Domain Chinese Word Segmentation'] | train.py tagger.py cws.py gcnn.py utils.py layer.py process_train_sentence read_train_file evaluator create_output data_iterator Embedding_layer GAN CRF_Layer gcnn GCNN_layer CRF_layer TextCNN_layer Embedding_layer create_input data_iterator Model create_dic create_mapping data_to_ids create_input data_iterator create_... | DAAT-CWS Coupling Distant Annotation and Adversarial Training for Cross-Domain Chinese Word Segmentation Paper accepted by ACL 2020 ### Prerequisites - python == 2.7 - tensorflow == 1.8.0 ### Dataset source domain dataset PKU and five distantly-annotated target datasets are put in `data/datasets` directory ### Usage R... | 79 |
Allen--Wu/Symmetry-Detection-of-Occluded-Point-Cloud-Using-Deep-Learning | ['symmetry detection'] | ['Symmetry Detection of Occluded Point Cloud Using Deep Learning'] | tools/_init_paths.py lib/transformations.py tools/pr-curve.py tools/train.py tools/pr-curve-occ.py lib/utils.py lib/knn/knn_pytorch/__init__.py lib/loss.py tools/eval_linemod.py lib/loss_refiner.py tools/train-gen.py tools/eval_ycb.py lib/network.py tools/train-gen_backup.py lib/extractors.py lib/knn/__init__.py lib/kn... | # Symmetry Detection of Occluded Point Cloud Using Deep Learning > Zhelun Wu, Hongyan Jiang, Siyun He > https://arxiv.org/pdf/2003.06520 ## Overview Symmetry detection has been a classical problem in computer graphics, many of which using traditional geometric methods. In recent years, however, we have witnessed the ar... | 80 |
Allen--Wu/dense_symmetry | ['symmetry detection'] | ['Symmetry Detection of Occluded Point Cloud Using Deep Learning'] | tools/_init_paths.py lib/transformations.py tools/pr-curve.py tools/train.py tools/pr-curve-occ.py lib/utils.py lib/knn/knn_pytorch/__init__.py lib/loss.py tools/eval_linemod.py lib/loss_refiner.py tools/train-gen.py tools/eval_ycb.py lib/network.py tools/train-gen_backup.py lib/extractors.py lib/knn/__init__.py lib/kn... | # Symmetry Detection of Occluded Point Cloud Using Deep Learning > Zhelun Wu, Hongyan Jiang, Siyun He > https://arxiv.org/pdf/2003.06520 ## Overview Symmetry detection has been a classical problem in computer graphics, many of which using traditional geometric methods. In recent years, however, we have witnessed the ar... | 81 |
AllenChen1998/AIEF | ['denoising', 'face verification'] | ['HRFA: High-Resolution Feature-based Attack'] | nets.py dnnlib/networks_stylegan.py dnnlib/perceptual_model.py dnnlib/tflib/tfutil.py dnnlib/util.py utils.py main.py dnnlib/__init__.py dnnlib/tflib/__init__.py dnnlib/tflib/network.py main create_variable_for_generator inception_resnet_v1 reduction_b Generator block8 block35 reduction_a create_stub inference block17 ... | AllenChen1998/AIEF | 82 |
Alpaca07/dtr | ['scene text recognition'] | ['Focus-Enhanced Scene Text Recognition with Deformable Convolutions'] | train.py torch_deform_conv/layers.py eval.py dataset.py models/deformable_crnn.py utils.py torch_deform_conv/deform_conv.py TestDataset LMDBDataset val weights_init train_batch averager AlignCollate loadData ResizeNormalize strLabelConverter DeformableCRNN ResidualBlock BidirectionalLSTM th_flatten th_batch_map_coordin... | # Deformable Text Recognition This software implements the Deformable Convolutional Recurrent Neural Network, a combination of of Convolutional Recurrent Neural Network, Deformable Convolutional Networks and Residual Blocks. Some of the codes are from [crnn.pytorch](https://github.com/meijieru/crnn.pytorch) and [Deform... | 83 |
Alpha-Video/AlphaVideo | ['action detection'] | ['Asynchronous Interaction Aggregation for Action Detection'] | alphavideo/mot/TubeTK/head.py alphavideo/mot/TubeTK/config.py alphavideo/model/TubeTK.py alphavideo/model/__init__.py alphavideo/model/AlphAction.py alphavideo/action/AlphAction/utils/misc.py alphavideo/action/AlphAction/utils/structures.py alphavideo/mot/TubeTK/TubeTK.py alphavideo/__init__.py alphavideo/loss/__init__... | ## Introduction AlphaVideo is an open-sourced video understanding toolbox based on [PyTorch](https://pytorch.org/) covering multi-object tracking and action detection. In AlphaVideo, we released the first one-stage multi-object tracking (MOT) system **TubeTK** that can achieve 66.9 MOTA on [MOT-16](https://motchallenge... | 84 |
AmingWu/CCN | ['visual commonsense reasoning'] | ['Connective Cognition Network for Directional Visual Commonsense Reasoning'] | utils/smalldetector.py utils/newdetector.py dataloaders/vcr.py utils/pytorch_misc.py utils/detector.py dataloaders/box_utils.py dataloaders/mask_utils.py config.py utils/testdetector.py dataloaders/bert_field.py train/train.py BertField load_image to_tensor_and_normalize resize_image make_mask _spaced_points VCR _fix_t... | # CCN ## Connective Cognition Network for Directional Visual Commonsense Reasoning (NeurIPS 2019)  Visual commonsense reasoning (VCR) has been introduced to boost research of cognition-level visual understanding, i.e... | 85 |
Ananaskelly/TPE | ['face verification'] | ['Triplet Probabilistic Embedding for Face Verification and Clustering'] | core/tpe_batcher.py utils/tf_utils.py core/metrics.py core/tpe_model.py experiments/main_cos_dist.py utils/data_processing.py calc_eer cos_similarity euclid_similarity TPEBatcher TPEModel generate_tuples cross_arrays load_data txt_to_numpy split_set bias_variable weight_variable_xavier weight_variable minimum min maxim... | # TPE https://arxiv.org/pdf/1604.05417.pdf | 86 |
AnastasisKratsios/NeurIPS2020_Non_Euclidean_Universal_Approximation_Example_DNN_Layer_Comparisons | ['gaussian processes'] | ['Non-Euclidean Universal Approximation'] | Init_Dump.py Helper_Functions.py Hyperparameter_Grid.py Optimal_Deep_Feature_and_Readout_Util.py Example.py Prepare_Data_California_Housing.py Helper_Utility.py def_trainable_layers_Nice_Input_Output build_and_predict_bad_model build_and_predict_Vanilla_model build_and_predict_nice_model def_trainable_layers_Randomized... | # NeurIPS - 2020: [Non Euclidean Universal Approximation](https://arxiv.org/abs/2006.02341) Coauthored by: - [Anastasis Kratsios](https://people.math.ethz.ch/~kratsioa/) - [Ievgen Bilokopytov](https://apps.ualberta.ca/directory/person/bilokopy) # Cite As: @inproceedings{NEURIPS2020_786ab8c4, author = {Kratsios... | 87 |
AndMu/Market-Wisdom | ['sentiment analysis'] | ['Market Trend Prediction using Sentiment Analysis: Lessons Learned and Paths Forward'] | src/PortfolioBasic/stockstats.py src/learning/__init__.py src/__init__.py src/utilities/Constants.py src/MarketData.py src/utilities/FileIterators.py src/DataLoader.py src/utilities/Utilities.py src/learning/BasicLearning.py src/utilities/TextHelper.py src/utilities/NumpyHelper.py src/utilities/DocumentExtractors.py sr... | # Market Trend Prediction using Sentiment Analysis: Lessons Learned and Paths Forward [Published Paper](wisdom_paper.pdf) Twitter bot running as [MarketPredGuy](https://twitter.com/MarketPredGuy) and its [code](https://github.com/AndMu/Wikiled.Market) ## *pSenti* Lexicon system * Download [*pSenti*](https://github.com/... | 88 |
AndersonPeng/imitation-learning-seq-conv | ['imitation learning'] | ['Imitation Learning for Sentence Generation with Dilated Convolutions Using Adversarial Training'] | distribs.py train_pg.py seq_conv_model.py test.py runner.py word2vec_model.py preprocess.py train_word2vec.py ops.py train_mle.py CategoricalDistrib DiagGaussianDistrib lstmCell sample_top single_layer_lstmCell static_lstm fc conv2d deconv2d conv1d layer_norm sample dynamic_lstm multi_layer_lstmCell tensor_array_lstm e... | # Imitation Learning for Sentence Generation with Dilated Convolutions Using Adversarial Training The source code for the paper "Imitation Learning for Sentence Generation with Dilated Convolutions Using Adversarial Training". <br> ## Prerequisites - [python >= 3.5.0](https://www.python.org/) - [tensorflow >= 1.8.0](ht... | 89 |
AndlollipopFU/PCB | ['person retrieval', 'person re identification', 'data augmentation'] | ['Camera Style Adaptation for Person Re-identification', 'Beyond Part Models: Person Retrieval with Refined Part Pooling (and a Strong Convolutional Baseline)'] | model/PCB/model.py model/ft_net_dense/model.py model/ft_ResNet50/train.py model/PCB/train.py model/ft_ResNet50/model.py random_erasing.py prepare.py train.py evaluate.py prepare_static.py test.py evaluate_rerank.py re_ranking.py model/fp16/train.py model.py demo.py model/ft_net_dense/train.py evaluate_gpu.py model/fp16... | <h1 align="center"> Person_reID_baseline_pytorch </h1> [](https://lgtm.com/projects/g/layumi/Person_reID_baseline_pytorch/context:python) [ "Anomaly Detection using Autoencoders in High Performance Computing Systems", Andrea Borghesi, Andrea Bartolini, Michele Lombardi, Michela Milano, Luca ... | 91 |
AndreaXu0401/ALIDRC | ['active learning', 'relation classification'] | ['Using active learning to expand training data for implicit discourse relation recognition'] | data_helpers.py active_learning_2.py train.py text_cnn.py dev_step train_step batch_iter load_data_and_labels build_input_data build_word_vocab_embd build_pos_vocab_embd load_pos_pkl clean_str load_word_pkl load_data_and_labels_discourse _variable_on_cpu TextCNN dev_step train_step print isoformat format run list batch... | Using Active Learning to Expand Training Data for Implicit Discourse Relation Recognition ===== It is slightly simplified implementation of our Using Active Learning to Expand Training Data for Implicit Discourse Relation Recognition paper in Tensorflow. Requirements ----- Python 3.5 Tensorflow 1.4 Numpy sklearn | 92 |
AndresPMD/StacMR | ['cross modal retrieval'] | ['StacMR: Scene-Text Aware Cross-Modal Retrieval'] | coco-caption/pycocoevalcap/__init__.py GCN_lib/Rs_GCN.py cocoapi-master/PythonAPI/pycocotools/coco.py models/S2VTModel.py models/DecoderRNN.py cocoapi-master/PythonAPI/build/lib.linux-x86_64-2.7/pycocotools/mask.py cocoapi-master/PythonAPI/pycocotools/mask.py coco-caption/pyciderevalcap/ciderD/ciderD.py opts.py CTC_img... | # StacMR (Scene Text Aware Cross Modal Retrieval) Dataset and code based on our WACV 2021 Accepted Paper: https://arxiv.org/abs/2012.04329 Official Website is online! https://europe.naverlabs.com/research/computer-vision/stacmr-scene-text-aware-cross-modal-retrieval/ Project is built on top of the [VSRN] (https://githu... | 93 |
Andrew-Qibin/SPNet | ['scene parsing', 'semantic segmentation'] | ['Strip Pooling: Rethinking Spatial Pooling for Scene Parsing'] | util/cityscapes.py lib/psa/modules/__init__.py lib/psa/functions/psamask.py lib/sync_bn/modules/sync_bn.py models/model_store.py lib/sync_bn/src/__init__.py models/spnet.py util/loss.py lib/psa/src/__init__.py lib/psa/functions/__init__.py lib/sync_bn/functions/__init__.py util/dataset.py lib/psa/modules/psamask.py mod... | # Strip Pooling: Rethinking Spatial Pooling for Scene Parsing
This repository is a PyTorch implementation for our [CVPR2020 paper](https://arxiv.org/pdf/2003.13328.pdf) (non-commercial use only).
The results reported in our paper are originally based on [PyTorch-Encoding](https://github.com/zhanghang1989/PyTorch-... | 94 |
AndrewJGaut/Towards-Understanding-Gender-Bias-in-Neural-Relation-Extraction | ['word embeddings', 'relation extraction', 'data augmentation'] | ['Towards Understanding Gender Bias in Relation Extraction'] | ModelResultParsing/Utility.py Models/OpenNRE/nrekit/nrekit/network/encoder.py Models/RESIDE/online/base_model.py WordEmbeddings/Word2VecTraining.py Models/OpenNRE/nrekit/nrekit/__init__.py Models/RESIDE/online/online_reside.py Models/OpenNRE/nrekit/nrekit/rl.py Models/RESIDE/online/pcnn.py ModelResultParsing/AttentionR... | # The source code for the paper titled "Towards Understanding Gender Bias in Neural Relation Extraction" by Tony Sun and Andrew Gaut et. al This code contains several different modules used for the experimentation given in the paper ## General Usage * Running full experiments * To run all experiments for varying enco... | 95 |
Andrewsher/X-Net | ['lesion segmentation', 'semantic segmentation'] | ['X-Net: Brain Stroke Lesion Segmentation Based on Depthwise Separable Convolution and Long-range Dependencies'] | loss.py FSM.py data.py utils.py main.py model.py train_data_generator val_data_generator create_val_date_generator create_train_date_generator fsm conv2d_bn_relu create_fsm_model dice get_loss dice_loss main train depth_conv_bn_relu x_block conv2d_bn_relu create_xception_unet_n get_score_for_one_patient get_score_from_... | # X-Net [X-Net: Brain Stroke Lesion Segmentation Based on Depthwise Separable Convolution and Long-range Dependencies (MICCAI 2019)](https://arxiv.org/abs/1907.07000) # 作者 Kehan Qi, Hao Yang, Cheng Li, Zaiyi Liu, Meiyun Wang, Qiegen Liu, and Shanshan Wang # 项目简介 ## 1. 功能 采用X-Net实现对ATLAS数据集的图像分割 ## 2. 性能 |Dice|IoU|Preci... | 96 |
Andy-CSKim/fashion-mnist | ['data augmentation'] | ['DENSER: Deep Evolutionary Network Structured Representation'] | utils/helper.py configs.py benchmark/convnet.py app.py benchmark/runner.py utils/argparser.py visualization/project_zalando.py utils/mnist_reader.py app_GD.py myNeuralNet myNeuralNet get_json_logger touch touch_dir _get_logger main cnn_model_fn PredictJob JobWorker JobManager get_args_request parse_arg get_args_cli now... | Andy-CSKim/fashion-mnist | 97 |
AndyShih12/SSDC | ['density estimation'] | ['Smoothing Structured Decomposable Circuits'] | CollapsedCompilation/order/order.py CollapsedCompilation/order/pybn/engines.py CollapsedCompilation/order/pybn/util.py CollapsedCompilation/order/pybn/learn.py CollapsedCompilation/order/pybn/networks/grid/grid.py CollapsedCompilation/order/pybn/net.py InverseAckermannCalculation/a.py CollapsedCompilation/order/pybn/ne... | AndyShih12/SSDC | 98 |
Anery/RSAN | ['joint entity and relation extraction', 'relation extraction'] | ['A Relation-Specific Attention Network for Joint Entity and Relation Extraction'] | train.py data/webnlg/util.py data_prepare.py data/multiNYT/util.py misc/utils.py networks/__init__.py misc/LossWrapper.py eval_utils.py DataLoader.py config.py networks/embedding.py networks/encoder.py data/webnlg/config.py model/__init__.py model/Rel_based_labeling.py networks/decoder.py Test.py parse_opt Data Loader ... | Source code for IJCAI 2020 paper "[A Relation-Specific Attention Network for Joint Entity and Relation Extraction](https://www.ijcai.org/Proceedings/2020/0561.pdf)" ## Prerequisites - Pytorch (1.0.1) - nltk - numpy - six ## Code ├── config.py ├── **data** ├── DataLoader.py | 99 |
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