{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/method/dropout/papers/265","list_of":"/method/dropout","method":"Dropout","archive":{"snapshot":"2025-07-28"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"date (newest first), then slug","page":265,"pages_in_order":275,"rows_per_page":100,"rows":[26401,26500],"of":27472,"counts":{"archive_papers_tagged":27472,"with_a_code_link":12129,"where_syntology_ran_a_sample":3620,"not_listed_spam_title":0,"listed":27472,"listed_where_code_ran":3620,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":3044,"every_run_a_failure_of_syntologys_instrument":576,"listed_with_a_run_with_no_instrument_failure":3044,"listed_every_run_a_failure_of_syntologys_instrument":576,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/method/dropout","prev":"/method/dropout/papers/264","next":"/method/dropout/papers/266","papers":[{"paper":"/paper/uni-em-an-environment-for-deep-neural-network","slug":"uni-em-an-environment-for-deep-neural-network","title":"UNI-EM: An Environment for Deep Neural Network-Based Automated Segmentation of Neuronal Electron Microscopic Images","date":"2019-04-12","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/an-empirical-study-of-spatial-attention","slug":"an-empirical-study-of-spatial-attention","title":"An Empirical Study of Spatial Attention Mechanisms in Deep Networks","date":"2019-04-11","arxiv_id":"1904.05873","n_code_links":1,"syntology":null},{"paper":"/paper/compressing-deep-neural-networks-by-matrix","slug":"compressing-deep-neural-networks-by-matrix","title":"Compressing deep neural networks by matrix product operators","date":"2019-04-11","arxiv_id":"1904.06194","n_code_links":1,"syntology":{"ran":2,"of":5,"n_ran_checked":1,"n_instrument":1,"unverified":3,"pointer_only":1,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","official":{"repos":["zfgao66/deeplearning-mpo"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":3,"ran_from_kinds":["official"]}}},{"paper":"/paper/190501962","slug":"190501962","title":"Harvey Mudd College at SemEval-2019 Task 4: The Clint Buchanan Hyperpartisan News Detector","date":"2019-04-10","arxiv_id":"1905.01962","n_code_links":1,"syntology":null},{"paper":"/paper/c3ae-exploring-the-limits-of-compact-model","slug":"c3ae-exploring-the-limits-of-compact-model","title":"C3AE: Exploring the Limits of Compact Model for Age Estimation","date":"2019-04-10","arxiv_id":"1904.05059","n_code_links":1,"syntology":null},{"paper":null,"slug":"dsnet-an-efficient-cnn-for-road-scene","title":"DSNet: An Efficient CNN for Road Scene Segmentation","date":"2019-04-10","arxiv_id":"1904.05022","n_code_links":0,"syntology":null},{"paper":null,"slug":"nlprsrpol-at-semeval-2019-task-6-and-task-5","title":"NLPR@SRPOL at SemEval-2019 Task 6 and Task 5: Linguistically enhanced deep learning offensive sentence classifier","date":"2019-04-10","arxiv_id":"1904.05152","n_code_links":0,"syntology":null},{"paper":"/paper/simple-bert-models-for-relation-extraction","slug":"simple-bert-models-for-relation-extraction","title":"Simple BERT Models for Relation Extraction and Semantic Role Labeling","date":"2019-04-10","arxiv_id":"1904.05255","n_code_links":3,"syntology":{"ran":3,"of":3,"n_ran_checked":3,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["Impavidity/relogic"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":"/paper/soft-conditional-computation","slug":"soft-conditional-computation","title":"CondConv: Conditionally Parameterized Convolutions for Efficient Inference","date":"2019-04-10","arxiv_id":"1904.04971","n_code_links":9,"syntology":null},{"paper":null,"slug":"a-new-gan-based-end-to-end-tts-training","title":"A New GAN-based End-to-End TTS Training Algorithm","date":"2019-04-09","arxiv_id":"1904.04775","n_code_links":0,"syntology":null},{"paper":"/paper/a-unified-model-for-joint-chinese-word","slug":"a-unified-model-for-joint-chinese-word","title":"A Graph-based Model for Joint Chinese Word Segmentation and Dependency Parsing","date":"2019-04-09","arxiv_id":"1904.04697","n_code_links":1,"syntology":null},{"paper":null,"slug":"novel-uncertainty-framework-for-deep-learning","title":"Novel Uncertainty Framework for Deep Learning Ensembles","date":"2019-04-09","arxiv_id":"1904.04917","n_code_links":0,"syntology":null},{"paper":"/paper/jointly-measuring-diversity-and-quality-in","slug":"jointly-measuring-diversity-and-quality-in","title":"Jointly Measuring Diversity and Quality in Text Generation Models","date":"2019-04-08","arxiv_id":"1904.03971","n_code_links":3,"syntology":{"ran":3,"of":6,"n_ran_checked":2,"n_instrument":1,"unverified":3,"pointer_only":1,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","official":{"repos":["IAmS4n/TextGenerationEvaluationMetrics"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"paper":null,"slug":"unsupervised-feature-learning-for","title":"Unsupervised Feature Learning for Environmental Sound Classification Using Weighted Cycle-Consistent Generative Adversarial Network","date":"2019-04-08","arxiv_id":"1904.04221","n_code_links":0,"syntology":null},{"paper":"/paper/adaptive-nms-refining-pedestrian-detection-in","slug":"adaptive-nms-refining-pedestrian-detection-in","title":"Adaptive NMS: Refining Pedestrian Detection in a Crowd","date":"2019-04-07","arxiv_id":"1904.03629","n_code_links":0,"syntology":null},{"paper":null,"slug":"identity-preserving-face-recovery-from-1","title":"Identity-preserving Face Recovery from Stylized Portraits","date":"2019-04-07","arxiv_id":"1904.04241","n_code_links":0,"syntology":null},{"paper":null,"slug":"effective-and-efficient-dropout-for-deep","title":"Effective and Efficient Dropout for Deep Convolutional Neural Networks","date":"2019-04-06","arxiv_id":"1904.03392","n_code_links":0,"syntology":null},{"paper":"/paper/publicly-available-clinical-bert-embeddings","slug":"publicly-available-clinical-bert-embeddings","title":"Publicly Available Clinical BERT Embeddings","date":"2019-04-06","arxiv_id":"1904.03323","n_code_links":3,"syntology":{"ran":2,"of":2,"n_ran_checked":1,"n_instrument":1,"unverified":0,"pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["EmilyAlsentzer/clinicalBERT"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official","unlocated"]}}},{"paper":null,"slug":"taco-vc-a-single-speaker-tacotron-based-voice","title":"Taco-VC: A Single Speaker Tacotron based Voice Conversion with Limited Data","date":"2019-04-06","arxiv_id":"1904.03522","n_code_links":0,"syntology":null},{"paper":null,"slug":"thisiscompetition-at-semeval-2019-task-9-bert","title":"ThisIsCompetition at SemEval-2019 Task 9: BERT is unstable for out-of-domain samples","date":"2019-04-06","arxiv_id":"1904.03339","n_code_links":0,"syntology":null},{"paper":"/paper/token-level-ensemble-distillation-for","slug":"token-level-ensemble-distillation-for","title":"Token-Level Ensemble Distillation for Grapheme-to-Phoneme Conversion","date":"2019-04-06","arxiv_id":"1904.03446","n_code_links":0,"syntology":null},{"paper":"/paper/um-iuling-at-semeval-2019-task-6-identifying","slug":"um-iuling-at-semeval-2019-task-6-identifying","title":"UM-IU@LING at SemEval-2019 Task 6: Identifying Offensive Tweets Using BERT and SVMs","date":"2019-04-06","arxiv_id":"1904.03450","n_code_links":1,"syntology":null},{"paper":null,"slug":"modeling-recurrence-for-transformer","title":"Modeling Recurrence for Transformer","date":"2019-04-05","arxiv_id":"1904.03092","n_code_links":0,"syntology":null},{"paper":null,"slug":"an-end-to-end-baseline-for-video-captioning","title":"End-to-End Video Captioning","date":"2019-04-04","arxiv_id":"1904.02628","n_code_links":0,"syntology":null},{"paper":null,"slug":"multi-reference-tacotron-by-intercross","title":"Multi-reference Tacotron by Intercross Training for Style Disentangling,Transfer and Control in Speech Synthesis","date":"2019-04-04","arxiv_id":"1904.02373","n_code_links":0,"syntology":null},{"paper":"/paper/unsupervised-domain-adaptation-of","slug":"unsupervised-domain-adaptation-of","title":"Unsupervised Domain Adaptation of Contextualized Embeddings for Sequence Labeling","date":"2019-04-04","arxiv_id":"1904.02817","n_code_links":1,"syntology":null},{"paper":null,"slug":"visualizing-attention-in-transformer-based","title":"Visualizing Attention in Transformer-Based Language Representation Models","date":"2019-04-04","arxiv_id":"1904.02679","n_code_links":0,"syntology":null},{"paper":"/paper/75-languages-1-model-parsing-universal","slug":"75-languages-1-model-parsing-universal","title":"75 Languages, 1 Model: Parsing Universal Dependencies Universally","date":"2019-04-03","arxiv_id":"1904.02099","n_code_links":3,"syntology":{"ran":9,"of":12,"n_ran_checked":9,"n_instrument":0,"unverified":3,"pointer_only":0,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","official":{"repos":["hyperparticle/udify"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":3,"ran_from_kinds":["official"]}}},{"paper":"/paper/bert-post-training-for-review-reading","slug":"bert-post-training-for-review-reading","title":"BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment Analysis","date":"2019-04-03","arxiv_id":"1904.02232","n_code_links":1,"syntology":{"ran":4,"of":7,"n_ran_checked":3,"n_instrument":1,"unverified":3,"pointer_only":1,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 1 honoured, 0 violated, 2 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","official":null}},{"paper":null,"slug":"hybrid-cosine-based-convolutional-neural","title":"Hybrid Cosine Based Convolutional Neural Networks","date":"2019-04-03","arxiv_id":"1904.01987","n_code_links":0,"syntology":null},{"paper":"/paper/probing-biomedical-embeddings-from-language","slug":"probing-biomedical-embeddings-from-language","title":"Probing Biomedical Embeddings from Language Models","date":"2019-04-03","arxiv_id":"1904.02181","n_code_links":1,"syntology":null},{"paper":"/paper/videobert-a-joint-model-for-video-and","slug":"videobert-a-joint-model-for-video-and","title":"VideoBERT: A Joint Model for Video and Language Representation Learning","date":"2019-04-03","arxiv_id":"1904.01766","n_code_links":3,"syntology":{"ran":1,"of":1,"n_ran_checked":1,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":null}},{"paper":null,"slug":"identifying-disease-free-chest-x-ray-images","title":"Identifying disease-free chest X-ray images with deep transfer learning","date":"2019-04-02","arxiv_id":"1904.01654","n_code_links":0,"syntology":null},{"paper":null,"slug":"why-resnet-works-residuals-generalize","title":"Why ResNet Works? Residuals Generalize","date":"2019-04-02","arxiv_id":"1904.01367","n_code_links":0,"syntology":null},{"paper":"/paper/reducing-bert-pre-training-time-from-3-days","slug":"reducing-bert-pre-training-time-from-3-days","title":"Large Batch Optimization for Deep Learning: Training BERT in 76 minutes","date":"2019-04-01","arxiv_id":"1904.00962","n_code_links":32,"syntology":{"ran":6,"of":11,"n_ran_checked":6,"n_instrument":0,"unverified":5,"pointer_only":9,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 5 unverified","official":{"repos":["tensorflow/addons"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":"/paper/regional-homogeneity-towards-learning","slug":"regional-homogeneity-towards-learning","title":"Regional Homogeneity: Towards Learning Transferable Universal Adversarial Perturbations Against Defenses","date":"2019-04-01","arxiv_id":"1904.00979","n_code_links":1,"syntology":{"ran":2,"of":2,"n_ran_checked":2,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["LiYingwei/Regional-Homogeneity"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"multi-vision-attention-networks-for-on-line","title":"Multi-vision Attention Networks for On-line Red Jujube Grading","date":"2019-03-31","arxiv_id":"1904.00388","n_code_links":0,"syntology":null},{"paper":"/paper/ana-at-semeval-2019-task-3-contextual-emotion","slug":"ana-at-semeval-2019-task-3-contextual-emotion","title":"ANA at SemEval-2019 Task 3: Contextual Emotion detection in Conversations through hierarchical LSTMs and BERT","date":"2019-03-30","arxiv_id":"1904.00132","n_code_links":1,"syntology":null},{"paper":null,"slug":"evaluating-cnns-on-the-gestalt-principle-of","title":"Evaluating CNNs on the Gestalt Principle of Closure","date":"2019-03-30","arxiv_id":"1904.00285","n_code_links":0,"syntology":null},{"paper":null,"slug":"joint-training-framework-for-text-to-speech","title":"Joint training framework for text-to-speech and voice conversion using multi-source Tacotron and WaveNet","date":"2019-03-29","arxiv_id":"1903.12389","n_code_links":0,"syntology":null},{"paper":null,"slug":"making-neural-machine-reading-comprehension","title":"Making Neural Machine Reading Comprehension Faster","date":"2019-03-29","arxiv_id":"1904.00796","n_code_links":0,"syntology":null},{"paper":"/paper/towards-knowledge-based-personalized-product","slug":"towards-knowledge-based-personalized-product","title":"Towards Knowledge-Based Personalized Product Description Generation in E-commerce","date":"2019-03-29","arxiv_id":"1903.12457","n_code_links":4,"syntology":null},{"paper":"/paper/atrial-fibrillation-detection-using-deep","slug":"atrial-fibrillation-detection-using-deep","title":"Atrial Fibrillation Detection Using Deep Features and Convolutional Networks","date":"2019-03-28","arxiv_id":"1903.11775","n_code_links":0,"syntology":null},{"paper":"/paper/benchmarking-neural-network-robustness-to-2","slug":"benchmarking-neural-network-robustness-to-2","title":"Benchmarking Neural Network Robustness to Common Corruptions and Perturbations","date":"2019-03-28","arxiv_id":"1903.12261","n_code_links":14,"syntology":{"ran":2,"of":3,"n_ran_checked":0,"n_instrument":2,"unverified":1,"pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","official":{"repos":["hendrycks/robustness"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":"/paper/distilling-task-specific-knowledge-from-bert","slug":"distilling-task-specific-knowledge-from-bert","title":"Distilling Task-Specific Knowledge from BERT into Simple Neural Networks","date":"2019-03-28","arxiv_id":"1903.12136","n_code_links":4,"syntology":{"ran":9,"of":9,"n_ran_checked":3,"n_instrument":6,"unverified":0,"pointer_only":0,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 3 honoured, 0 violated, 0 with no contract checked; 6 where Syntology's instrument failed) · 0 unverified","official":null}},{"paper":"/paper/train-sort-explain-learning-to-diagnose","slug":"train-sort-explain-learning-to-diagnose","title":"Train, Sort, Explain: Learning to Diagnose Translation Models","date":"2019-03-28","arxiv_id":"1903.12017","n_code_links":1,"syntology":null},{"paper":null,"slug":"190410045","title":"Automatic Spelling Correction with Transformer for CTC-based End-to-End Speech Recognition","date":"2019-03-27","arxiv_id":"1904.10045","n_code_links":0,"syntology":null},{"paper":"/paper/network-slimming-by-slimmable-networks","slug":"network-slimming-by-slimmable-networks","title":"AutoSlim: Towards One-Shot Architecture Search for Channel Numbers","date":"2019-03-27","arxiv_id":"1903.11728","n_code_links":10,"syntology":null},{"paper":null,"slug":"understanding-unconventional-preprocessors-in","title":"Understanding Unconventional Preprocessors in Deep Convolutional Neural Networks for Face Identification","date":"2019-03-27","arxiv_id":"1904.00815","n_code_links":0,"syntology":null},{"paper":"/paper/scibert-pretrained-contextualized-embeddings","slug":"scibert-pretrained-contextualized-embeddings","title":"SciBERT: A Pretrained Language Model for Scientific Text","date":"2019-03-26","arxiv_id":"1903.10676","n_code_links":6,"syntology":null},{"paper":"/paper/simple-applications-of-bert-for-ad-hoc","slug":"simple-applications-of-bert-for-ad-hoc","title":"Simple Applications of BERT for Ad Hoc Document Retrieval","date":"2019-03-26","arxiv_id":"1903.10972","n_code_links":2,"syntology":null},{"paper":null,"slug":"apple-leaf-disease-identification-through","title":"Apple Leaf Disease Identification through Region-of-Interest-Aware Deep Convolutional Neural Network","date":"2019-03-25","arxiv_id":"1903.10356","n_code_links":0,"syntology":null},{"paper":"/paper/fine-tune-bert-for-extractive-summarization","slug":"fine-tune-bert-for-extractive-summarization","title":"Fine-tune BERT for Extractive Summarization","date":"2019-03-25","arxiv_id":"1903.10318","n_code_links":12,"syntology":{"ran":5,"of":5,"n_ran_checked":4,"n_instrument":1,"unverified":0,"pointer_only":1,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 1 violated, 3 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["nlpyang/BertSum"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":null,"slug":"knowledge-driven-encode-retrieve-paraphrase","title":"Knowledge-driven Encode, Retrieve, Paraphrase for Medical Image Report Generation","date":"2019-03-25","arxiv_id":"1903.10122","n_code_links":0,"syntology":null},{"paper":"/paper/on-measuring-social-biases-in-sentence","slug":"on-measuring-social-biases-in-sentence","title":"On Measuring Social Biases in Sentence Encoders","date":"2019-03-25","arxiv_id":"1903.10561","n_code_links":1,"syntology":null},{"paper":"/paper/recognizing-arrow-of-time-in-the-short","slug":"recognizing-arrow-of-time-in-the-short","title":"Recognizing Arrow Of Time In The Short Stories","date":"2019-03-25","arxiv_id":"1903.10548","n_code_links":1,"syntology":null},{"paper":"/paper/spatially-adaptive-residual-networks-for","slug":"spatially-adaptive-residual-networks-for","title":"Motion Deblurring with an Adaptive Network","date":"2019-03-25","arxiv_id":"1903.11394","n_code_links":0,"syntology":null},{"paper":"/paper/srgan-training-dataset-matters","slug":"srgan-training-dataset-matters","title":"SRGAN: Training Dataset Matters","date":"2019-03-24","arxiv_id":"1903.09922","n_code_links":1,"syntology":null},{"paper":"/paper/auto-reid-searching-for-a-part-aware-convnet","slug":"auto-reid-searching-for-a-part-aware-convnet","title":"Auto-ReID: Searching for a Part-aware ConvNet for Person Re-Identification","date":"2019-03-23","arxiv_id":"1903.09776","n_code_links":3,"syntology":null},{"paper":null,"slug":"bitsplit-net-multi-bit-deep-neural-network","title":"BitSplit-Net: Multi-bit Deep Neural Network with Bitwise Activation Function","date":"2019-03-23","arxiv_id":"1903.09807","n_code_links":0,"syntology":null},{"paper":"/paper/photorealistic-style-transfer-via-wavelet","slug":"photorealistic-style-transfer-via-wavelet","title":"Photorealistic Style Transfer via Wavelet Transforms","date":"2019-03-23","arxiv_id":"1903.09760","n_code_links":4,"syntology":{"ran":10,"of":10,"n_ran_checked":7,"n_instrument":3,"unverified":0,"pointer_only":3,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 3 honoured, 1 violated, 3 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","official":{"repos":["clovaai/WCT2"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"paper":"/paper/progressive-dnn-compression-a-key-to-achieve","slug":"progressive-dnn-compression-a-key-to-achieve","title":"Progressive DNN Compression: A Key to Achieve Ultra-High Weight Pruning and Quantization Rates using ADMM","date":"2019-03-23","arxiv_id":"1903.09769","n_code_links":2,"syntology":null},{"paper":"/paper/utilizing-bert-for-aspect-based-sentiment","slug":"utilizing-bert-for-aspect-based-sentiment","title":"Utilizing BERT for Aspect-Based Sentiment Analysis via Constructing Auxiliary Sentence","date":"2019-03-22","arxiv_id":"1903.09588","n_code_links":8,"syntology":{"ran":19,"of":27,"n_ran_checked":15,"n_instrument":4,"unverified":8,"pointer_only":9,"phrase":"19 ran (of which 0 constructed an object rather than computing a result; 15 with no instrument failure: 3 honoured, 0 violated, 12 with no contract checked; 4 where Syntology's instrument failed) · 8 unverified","official":{"repos":["HSLCY/ABSA-BERT-pair"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":2,"ran_from_kinds":["listed","official"]}}},{"paper":null,"slug":"learning-multi-level-information-for-dialogue","title":"Learning Multi-Level Information for Dialogue Response Selection by Highway Recurrent Transformer","date":"2019-03-21","arxiv_id":"1903.08953","n_code_links":0,"syntology":null},{"paper":null,"slug":"linguistic-knowledge-and-transferability-of","title":"Linguistic Knowledge and Transferability of Contextual Representations","date":"2019-03-21","arxiv_id":"1903.08855","n_code_links":0,"syntology":null},{"paper":"/paper/low-resource-text-classification-with-ulmfit","slug":"low-resource-text-classification-with-ulmfit","title":"Low Resource Text Classification with ULMFit and Backtranslation","date":"2019-03-21","arxiv_id":"1903.09244","n_code_links":1,"syntology":null},{"paper":"/paper/selective-attention-for-context-aware-neural","slug":"selective-attention-for-context-aware-neural","title":"Selective Attention for Context-aware Neural Machine Translation","date":"2019-03-21","arxiv_id":"1903.08788","n_code_links":1,"syntology":null},{"paper":"/paper/cloze-driven-pretraining-of-self-attention","slug":"cloze-driven-pretraining-of-self-attention","title":"Cloze-driven Pretraining of Self-attention Networks","date":"2019-03-19","arxiv_id":"1903.07785","n_code_links":0,"syntology":null},{"paper":null,"slug":"advanced-capsule-networks-via-context","title":"Advanced Capsule Networks via Context Awareness","date":"2019-03-18","arxiv_id":"1903.07497","n_code_links":0,"syntology":null},{"paper":null,"slug":"deep-learning-enables-automatic-detection-and","title":"Deep Learning Enables Automatic Detection and Segmentation of Brain Metastases on Multi-Sequence MRI","date":"2019-03-18","arxiv_id":"1903.07988","n_code_links":0,"syntology":null},{"paper":"/paper/neutron-an-implementation-of-the-transformer","slug":"neutron-an-implementation-of-the-transformer","title":"Neutron: An Implementation of the Transformer Translation Model and its Variants","date":"2019-03-18","arxiv_id":"1903.07402","n_code_links":2,"syntology":null},{"paper":null,"slug":"stnreid-deep-convolutional-networks-with","title":"STNReID : Deep Convolutional Networks with Pairwise Spatial Transformer Networks for Partial Person Re-identification","date":"2019-03-17","arxiv_id":"1903.07072","n_code_links":0,"syntology":null},{"paper":null,"slug":"swcaffe-a-parallel-framework-for-accelerating","title":"swCaffe: a Parallel Framework for Accelerating Deep Learning Applications on Sunway TaihuLight","date":"2019-03-16","arxiv_id":"1903.06934","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-deep-patent-landscaping-model-using","title":"Deep Patent Landscaping Model Using Transformer and Graph Embedding","date":"2019-03-14","arxiv_id":"1903.05823","n_code_links":0,"syntology":null},{"paper":"/paper/episodic-memory-reader-learning-what-to","slug":"episodic-memory-reader-learning-what-to","title":"Episodic Memory Reader: Learning What to Remember for Question Answering from Streaming Data","date":"2019-03-14","arxiv_id":"1903.06164","n_code_links":1,"syntology":null},{"paper":null,"slug":"inefficiency-of-k-fac-for-large-batch-size","title":"Inefficiency of K-FAC for Large Batch Size Training","date":"2019-03-14","arxiv_id":"1903.06237","n_code_links":0,"syntology":null},{"paper":"/paper/communication-efficient-distributed-sgd-with","slug":"communication-efficient-distributed-sgd-with","title":"Communication-efficient distributed SGD with Sketching","date":"2019-03-12","arxiv_id":"1903.04488","n_code_links":2,"syntology":{"ran":1,"of":1,"n_ran_checked":0,"n_instrument":1,"unverified":0,"pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["dhroth/sketchedsgd"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/hetconv-heterogeneous-kernel-based","slug":"hetconv-heterogeneous-kernel-based","title":"HetConv: Heterogeneous Kernel-Based Convolutions for Deep CNNs","date":"2019-03-11","arxiv_id":"1903.04120","n_code_links":1,"syntology":null},{"paper":null,"slug":"hltsuda-at-semeval-2019-task-1-ucca-graph","title":"HLT@SUDA at SemEval 2019 Task 1: UCCA Graph Parsing as Constituent Tree Parsing","date":"2019-03-11","arxiv_id":"1903.04153","n_code_links":0,"syntology":null},{"paper":null,"slug":"scene-memory-transformer-for-embodied-agents","title":"Scene Memory Transformer for Embodied Agents in Long-Horizon Tasks","date":"2019-03-09","arxiv_id":"1903.03878","n_code_links":0,"syntology":null},{"paper":"/paper/image-privacy-prediction-using-deep-neural","slug":"image-privacy-prediction-using-deep-neural","title":"Image Privacy Prediction Using Deep Neural Networks","date":"2019-03-08","arxiv_id":"1903.03695","n_code_links":1,"syntology":null},{"paper":"/paper/self-tuning-networks-bilevel-optimization-of","slug":"self-tuning-networks-bilevel-optimization-of","title":"Self-Tuning Networks: Bilevel Optimization of Hyperparameters using Structured Best-Response Functions","date":"2019-03-07","arxiv_id":"1903.03088","n_code_links":3,"syntology":null},{"paper":null,"slug":"dixit-interactive-visual-storytelling-via","title":"Dixit: Interactive Visual Storytelling via Term Manipulation","date":"2019-03-06","arxiv_id":"1903.02230","n_code_links":0,"syntology":null},{"paper":null,"slug":"gq-stn-optimizing-one-shot-grasp-detection","title":"GQ-STN: Optimizing One-Shot Grasp Detection based on Robustness Classifier","date":"2019-03-06","arxiv_id":"1903.02489","n_code_links":0,"syntology":null},{"paper":null,"slug":"prostate-segmentation-from-3d-mri-using-a-two","title":"Prostate Segmentation from 3D MRI Using a Two-Stage Model and Variable-Input Based Uncertainty Measure","date":"2019-03-06","arxiv_id":"1903.02500","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-densenet-based-approach-for-multi-frame-in","title":"A DenseNet Based Approach for Multi-Frame In-Loop Filter in HEVC","date":"2019-03-05","arxiv_id":"1903.01648","n_code_links":0,"syntology":null},{"paper":"/paper/self-adversarial-variational-autoencoder-with","slug":"self-adversarial-variational-autoencoder-with","title":"adVAE: A self-adversarial variational autoencoder with Gaussian anomaly prior knowledge for anomaly detection","date":"2019-03-03","arxiv_id":"1903.00904","n_code_links":2,"syntology":null},{"paper":"/paper/gap-generalizable-approximate-graph","slug":"gap-generalizable-approximate-graph","title":"GAP: Generalizable Approximate Graph Partitioning Framework","date":"2019-03-02","arxiv_id":"1903.00614","n_code_links":1,"syntology":null},{"paper":"/paper/frequency-domain-transformer-networks-for","slug":"frequency-domain-transformer-networks-for","title":"Frequency Domain Transformer Networks for Video Prediction","date":"2019-03-01","arxiv_id":"1903.00271","n_code_links":1,"syntology":null},{"paper":"/paper/bert-for-joint-intent-classification-and-slot","slug":"bert-for-joint-intent-classification-and-slot","title":"BERT for Joint Intent Classification and Slot Filling","date":"2019-02-28","arxiv_id":"1902.10909","n_code_links":16,"syntology":{"ran":26,"of":32,"n_ran_checked":19,"n_instrument":7,"unverified":6,"pointer_only":5,"phrase":"26 ran (of which 0 constructed an object rather than computing a result; 19 with no instrument failure: 1 honoured, 0 violated, 18 with no contract checked; 7 where Syntology's instrument failed) · 6 unverified","official":null}},{"paper":"/paper/generative-collaborative-networks-for-single","slug":"generative-collaborative-networks-for-single","title":"Generative Collaborative Networks for Single Image Super-Resolution","date":"2019-02-27","arxiv_id":"1902.10467","n_code_links":1,"syntology":null},{"paper":"/paper/how-large-a-vocabulary-does-text","slug":"how-large-a-vocabulary-does-text","title":"How Large a Vocabulary Does Text Classification Need? A Variational Approach to Vocabulary Selection","date":"2019-02-27","arxiv_id":"1902.10339","n_code_links":1,"syntology":{"ran":3,"of":3,"n_ran_checked":3,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["wenhuchen/Variational-Vocabulary-Selection"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"stochastically-rank-regularized-tensor","title":"Tensor Dropout for Robust Learning","date":"2019-02-27","arxiv_id":"1902.10758","n_code_links":0,"syntology":null},{"paper":"/paper/attentional-encoder-network-for-targeted","slug":"attentional-encoder-network-for-targeted","title":"Attentional Encoder Network for Targeted Sentiment Classification","date":"2019-02-25","arxiv_id":"1902.09314","n_code_links":5,"syntology":null},{"paper":"/paper/pretraining-based-natural-language-generation","slug":"pretraining-based-natural-language-generation","title":"Pretraining-Based Natural Language Generation for Text Summarization","date":"2019-02-25","arxiv_id":"1902.09243","n_code_links":4,"syntology":{"ran":1,"of":1,"n_ran_checked":0,"n_instrument":1,"unverified":0,"pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":null}},{"paper":"/paper/star-transformer","slug":"star-transformer","title":"Star-Transformer","date":"2019-02-25","arxiv_id":"1902.09113","n_code_links":2,"syntology":{"ran":0,"of":1,"n_ran_checked":0,"n_instrument":0,"unverified":1,"pointer_only":0,"phrase":"0 ran · 1 unverified","official":{"repos":["dmlc/dgl"],"state":"official: harvested for another paper","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":[]}}},{"paper":"/paper/enhancing-clinical-concept-extraction-with","slug":"enhancing-clinical-concept-extraction-with","title":"Enhancing Clinical Concept Extraction with Contextual Embeddings","date":"2019-02-22","arxiv_id":"1902.08691","n_code_links":0,"syntology":null},{"paper":null,"slug":"jointly-sparse-convolutional-neural-networks","title":"Jointly Sparse Convolutional Neural Networks in Dual Spatial-Winograd Domains","date":"2019-02-21","arxiv_id":"1902.08192","n_code_links":0,"syntology":null},{"paper":"/paper/a-novel-adaptive-learning-rate-scheduler-for","slug":"a-novel-adaptive-learning-rate-scheduler-for","title":"LipschitzLR: Using theoretically computed adaptive learning rates for fast convergence","date":"2019-02-20","arxiv_id":"1902.07399","n_code_links":5,"syntology":null},{"paper":"/paper/dnnvm-end-to-end-compiler-leveraging","slug":"dnnvm-end-to-end-compiler-leveraging","title":"DNNVM : End-to-End Compiler Leveraging Heterogeneous Optimizations on FPGA-based CNN Accelerators","date":"2019-02-20","arxiv_id":"1902.07463","n_code_links":1,"syntology":null}],"record_sha256":"9107b693c88a7677c9a33bbe1f8274dea3b74fca67b23f1d0aaad0ba67a2601f","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}