{"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/softmax/papers/318","list_of":"/method/softmax","method":"Softmax","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":318,"pages_in_order":375,"rows_per_page":100,"rows":[31701,31800],"of":37443,"counts":{"archive_papers_tagged":37443,"with_a_code_link":15869,"where_syntology_ran_a_sample":4578,"not_listed_spam_title":0,"listed":37443,"listed_where_code_ran":4578,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":3835,"every_run_a_failure_of_syntologys_instrument":743,"listed_with_a_run_with_no_instrument_failure":3835,"listed_every_run_a_failure_of_syntologys_instrument":743,"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/softmax","prev":"/method/softmax/papers/317","next":"/method/softmax/papers/319","papers":[{"paper":null,"slug":"text-simplification-with-reinforcement","title":"Text Simplification with Reinforcement Learning Using Supervised Rewards on Grammaticality, Meaning Preservation, and Simplicity","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"textlearner-at-semeval-2020-task-10-a","title":"TextLearner at SemEval-2020 Task 10: A Contextualized Ranking System in Solving Emphasis Selection in Text","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"thenorth-at-semeval-2020-task-12-hate-speech","title":"TheNorth at SemEval-2020 Task 12: Hate Speech Detection Using RoBERTa","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"timbert-toponym-identifier-for-the-medical","title":"TIMBERT: Toponym Identifier For The Medical Domain Based on BERT","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"touch-editing-a-flexible-one-time-interaction","title":"Touch Editing: A Flexible One-Time Interaction Approach for Translation","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/trainx-named-entity-linking-with-active","slug":"trainx-named-entity-linking-with-active","title":"TrainX -- Named Entity Linking with Active Sampling and Bi-Encoders","date":"2020-12-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"transformers-at-semeval-2020-task-11","title":"Transformers at SemEval-2020 Task 11: Propaganda Fragment Detection Using Diversified BERT Architectures Based Ensemble Learning","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"ttui-at-semeval-2020-task-11-propaganda","title":"TTUI at SemEval-2020 Task 11: Propaganda Detection with Transfer Learning and Ensembles","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"tue-at-semeval-2020-task-1-detecting-semantic","title":"TUE at SemEval-2020 Task 1: Detecting Semantic Change by Clustering Contextual Word Embeddings","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"ui-at-semeval-2020-task-4-commonsense","title":"UI at SemEval-2020 Task 4: Commonsense Validation and Explanation by Exploiting Contradiction","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"ujnlp-at-semeval-2020-task-12-detecting","title":"UJNLP at SemEval-2020 Task 12: Detecting Offensive Language Using Bidirectional Transformers","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"umsiforeseer-at-semeval-2020-task-11","title":"UMSIForeseer at SemEval-2020 Task 11: Propaganda Detection by Fine-Tuning BERT with Resampling and Ensemble Learning","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"unituebingencl-at-semeval-2020-task-7-humor","title":"UniTuebingenCL at SemEval-2020 Task 7: Humor Detection in News Headlines","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"unixlong-at-semeval-2020-task-6-a-joint-model","title":"UNIXLONG at SemEval-2020 Task 6: A Joint Model for Definition Extraction","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"uor-at-semeval-2020-task-4-pre-trained","title":"UoR at SemEval-2020 Task 4: Pre-trained Sentence Transformer Models for Commonsense Validation and Explanation","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/uwsod-toward-fully-supervised-level-capacity","slug":"uwsod-toward-fully-supervised-level-capacity","title":"UWSOD: Toward Fully-Supervised-Level Capacity Weakly Supervised Object Detection","date":"2020-12-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"uzh-at-semeval-2020-task-3-combining-bert","title":"UZH at SemEval-2020 Task 3: Combining BERT with WordNet Sense Embeddings to Predict Graded Word Similarity Changes","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"warren-at-semeval-2020-task-4-albert-and","title":"Warren at SemEval-2020 Task 4: ALBERT and Multi-Task Learning for Commonsense Validation","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"will-go-at-semeval-2020-task-3-an-accurate","title":"Will\\_Go at SemEval-2020 Task 3: An Accurate Model for Predicting the (Graded) Effect of Context in Word Similarity Based on BERT","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"will-go-at-semeval-2020-task-9-an-accurate","title":"Will\\_go at SemEval-2020 Task 9: An Accurate Approach for Sentiment Analysis on Hindi-English Tweets Based on Bert and Pesudo Label Strategy","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"wmd-at-semeval-2020-tasks-7-and-11-assessing","title":"WMD at SemEval-2020 Tasks 7 and 11: Assessing Humor and Propaganda Using Unsupervised Data Augmentation","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"wuy-at-semeval-2020-task-7-combining-bert-and","title":"WUY at SemEval-2020 Task 7: Combining BERT and Naive Bayes-SVM for Humor Assessment in Edited News Headlines","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"ynu-hpcc-at-semeval-2020-task-7-using-an","title":"YNU-HPCC at SemEval-2020 Task 7: Using an Ensemble BiGRU Model to Evaluate the Humor of Edited News Titles","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"ynu-oxz-at-semeval-2020-task-4-commonsense","title":"YNU-oxz at SemEval-2020 Task 4: Commonsense Validation Using BERT with Bidirectional GRU","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"ynutaoxin-at-semeval-2020-task-11","title":"YNUtaoxin at SemEval-2020 Task 11: Identification Fragments of Propaganda Technique by Neural Sequence Labeling Models with Different Tagging Schemes and Pre-trained Language Model","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"you-may-like-this-hotel-because-identifying","title":"You May Like This Hotel Because ...: Identifying Evidence for Explainable Recommendations","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"afd-net-adaptive-fully-dual-network-for-few","title":"AFD-Net: Adaptive Fully-Dual Network for Few-Shot Object Detection","date":"2020-11-30","arxiv_id":"2011.14667","n_code_links":0,"syntology":null},{"paper":null,"slug":"extracting-electron-scattering-cross-sections","title":"Extracting Electron Scattering Cross Sections from Swarm Data using Deep Neural Networks","date":"2020-11-30","arxiv_id":"2011.14711","n_code_links":0,"syntology":null},{"paper":null,"slug":"fake-news-detection-in-social-media-using","title":"Fake News Detection in Social Media using Graph Neural Networks and NLP Techniques: A COVID-19 Use-case","date":"2020-11-30","arxiv_id":"2012.07517","n_code_links":0,"syntology":null},{"paper":"/paper/feature-learning-in-infinite-width-neural","slug":"feature-learning-in-infinite-width-neural","title":"Feature Learning in Infinite-Width Neural Networks","date":"2020-11-30","arxiv_id":"2011.14522","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":{"repos":["edwardjhu/TP4"],"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":"flood-detection-via-twitter-streams-using","title":"Flood Detection via Twitter Streams using Textual and Visual Features","date":"2020-11-30","arxiv_id":"2011.14944","n_code_links":0,"syntology":null},{"paper":null,"slug":"floods-detection-in-twitter-text-and-images","title":"Floods Detection in Twitter Text and Images","date":"2020-11-30","arxiv_id":"2011.14943","n_code_links":0,"syntology":null},{"paper":"/paper/machine-translation-of-novels-in-the-age-of","slug":"machine-translation-of-novels-in-the-age-of","title":"Machine Translation of Novels in the Age of Transformer","date":"2020-11-30","arxiv_id":"2011.14979","n_code_links":1,"syntology":null},{"paper":null,"slug":"multi-modal-detection-of-alzheimer-s-disease","title":"Multi-Modal Detection of Alzheimer's Disease from Speech and Text","date":"2020-11-30","arxiv_id":"2012.00096","n_code_links":0,"syntology":null},{"paper":"/paper/scalenas-one-shot-learning-of-scale-aware","slug":"scalenas-one-shot-learning-of-scale-aware","title":"ScaleNAS: One-Shot Learning of Scale-Aware Representations for Visual Recognition","date":"2020-11-30","arxiv_id":"2011.14584","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-novel-sentiment-analysis-engine-for","title":"A Novel Sentiment Analysis Engine for Preliminary Depression Status Estimation on Social Media","date":"2020-11-29","arxiv_id":"2011.14280","n_code_links":0,"syntology":null},{"paper":"/paper/coarse-to-fine-memory-matching-for-joint","slug":"coarse-to-fine-memory-matching-for-joint","title":"Coarse-to-Fine Memory Matching for Joint Retrieval and Classification","date":"2020-11-29","arxiv_id":"2012.02287","n_code_links":1,"syntology":null},{"paper":"/paper/detecting-video-game-player-burnout-with-the","slug":"detecting-video-game-player-burnout-with-the","title":"Detecting Video Game Player Burnout with the Use of Sensor Data and Machine Learning","date":"2020-11-29","arxiv_id":"2012.02299","n_code_links":1,"syntology":null},{"paper":"/paper/fully-quantized-image-super-resolution","slug":"fully-quantized-image-super-resolution","title":"Fully Quantized Image Super-Resolution Networks","date":"2020-11-29","arxiv_id":"2011.14265","n_code_links":1,"syntology":null},{"paper":null,"slug":"generative-pre-training-for-paraphrase","title":"Generative Pre-training for Paraphrase Generation by Representing and Predicting Spans in Exemplars","date":"2020-11-29","arxiv_id":"2011.14344","n_code_links":0,"syntology":null},{"paper":"/paper/improved-semantic-role-labeling-using","slug":"improved-semantic-role-labeling-using","title":"Improved Semantic Role Labeling using Parameterized Neighborhood Memory Adaptation","date":"2020-11-29","arxiv_id":"2011.14459","n_code_links":1,"syntology":null},{"paper":"/paper/adabins-depth-estimation-using-adaptive-bins","slug":"adabins-depth-estimation-using-adaptive-bins","title":"AdaBins: Depth Estimation using Adaptive Bins","date":"2020-11-28","arxiv_id":"2011.14141","n_code_links":11,"syntology":null},{"paper":null,"slug":"edgebert-optimizing-on-chip-inference-for","title":"EdgeBERT: Sentence-Level Energy Optimizations for Latency-Aware Multi-Task NLP Inference","date":"2020-11-28","arxiv_id":"2011.14203","n_code_links":0,"syntology":null},{"paper":null,"slug":"transformer-query-target-knowledge-discovery","title":"Transformer Query-Target Knowledge Discovery (TEND): Drug Discovery from CORD-19","date":"2020-11-28","arxiv_id":"2012.04682","n_code_links":0,"syntology":null},{"paper":"/paper/understanding-how-bert-learns-to-identify","slug":"understanding-how-bert-learns-to-identify","title":"An Investigation of Language Model Interpretability via Sentence Editing","date":"2020-11-28","arxiv_id":"2011.14039","n_code_links":2,"syntology":null},{"paper":null,"slug":"chinese-medical-question-answer-matching","title":"Chinese Medical Question Answer Matching Based on Interactive Sentence Representation Learning","date":"2020-11-27","arxiv_id":"2011.13573","n_code_links":0,"syntology":null},{"paper":null,"slug":"core-an-efficient-coarse-refined-training","title":"CoRe: An Efficient Coarse-refined Training Framework for BERT","date":"2020-11-27","arxiv_id":"2011.13633","n_code_links":0,"syntology":null},{"paper":"/paper/general-multi-label-image-classification-with","slug":"general-multi-label-image-classification-with","title":"General Multi-label Image Classification with Transformers","date":"2020-11-27","arxiv_id":"2011.14027","n_code_links":2,"syntology":null},{"paper":null,"slug":"progressively-stacking-2-0-a-multi-stage-1","title":"Progressively Stacking 2.0: A Multi-stage Layerwise Training Method for BERT Training Speedup","date":"2020-11-27","arxiv_id":"2011.13635","n_code_links":0,"syntology":null},{"paper":null,"slug":"self-emd-self-supervised-object-detection","title":"Self-EMD: Self-Supervised Object Detection without ImageNet","date":"2020-11-27","arxiv_id":"2011.13677","n_code_links":0,"syntology":null},{"paper":null,"slug":"temporal-channel-transformer-for-3d-lidar","title":"Temporal-Channel Transformer for 3D Lidar-Based Video Object Detection in Autonomous Driving","date":"2020-11-27","arxiv_id":"2011.13628","n_code_links":0,"syntology":null},{"paper":"/paper/tstarbot-x-an-open-sourced-and-comprehensive","slug":"tstarbot-x-an-open-sourced-and-comprehensive","title":"TStarBot-X: An Open-Sourced and Comprehensive Study for Efficient League Training in StarCraft II Full Game","date":"2020-11-27","arxiv_id":"2011.13729","n_code_links":1,"syntology":null},{"paper":"/paper/a-recurrent-vision-and-language-bert-for","slug":"a-recurrent-vision-and-language-bert-for","title":"A Recurrent Vision-and-Language BERT for Navigation","date":"2020-11-26","arxiv_id":"2011.13922","n_code_links":1,"syntology":null},{"paper":null,"slug":"automatic-detection-of-cardiac-chambers-using","title":"Automatic Detection of Cardiac Chambers Using an Attention-based YOLOv4 Framework from Four-chamber View of Fetal Echocardiography","date":"2020-11-26","arxiv_id":"2011.13096","n_code_links":0,"syntology":null},{"paper":"/paper/channel-wise-distillation-for-semantic","slug":"channel-wise-distillation-for-semantic","title":"Channel-wise Knowledge Distillation for Dense Prediction","date":"2020-11-26","arxiv_id":"2011.13256","n_code_links":3,"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":["drilistbox/CWD"],"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/data-efficient-classification-of-radio","slug":"data-efficient-classification-of-radio","title":"Data-Efficient Classification of Radio Galaxies","date":"2020-11-26","arxiv_id":"2011.13311","n_code_links":1,"syntology":null},{"paper":null,"slug":"encoding-syntactic-constituency-paths-for","title":"Encoding Syntactic Constituency Paths for Frame-Semantic Parsing with Graph Convolutional Networks","date":"2020-11-26","arxiv_id":"2011.13210","n_code_links":0,"syntology":null},{"paper":"/paper/molecular-representation-learning-with","slug":"molecular-representation-learning-with","title":"Molecular representation learning with language models and domain-relevant auxiliary tasks","date":"2020-11-26","arxiv_id":"2011.13230","n_code_links":2,"syntology":null},{"paper":"/paper/omni-gan-on-the-secrets-of-cgans-and-beyond","slug":"omni-gan-on-the-secrets-of-cgans-and-beyond","title":"Omni-GAN: On the Secrets of cGANs and Beyond","date":"2020-11-26","arxiv_id":"2011.13074","n_code_links":3,"syntology":null},{"paper":null,"slug":"the-devil-is-in-the-boundary-exploiting","title":"The Devil is in the Boundary: Exploiting Boundary Representation for Basis-based Instance Segmentation","date":"2020-11-26","arxiv_id":"2011.13241","n_code_links":0,"syntology":null},{"paper":null,"slug":"transformer-based-models-for-automatic","title":"Transformer-Based Models for Automatic Identification of Argument Relations: A Cross-Domain Evaluation","date":"2020-11-26","arxiv_id":"2011.13187","n_code_links":0,"syntology":null},{"paper":"/paper/two-stage-transformer-model-for-covid-19-fake","slug":"two-stage-transformer-model-for-covid-19-fake","title":"Two Stage Transformer Model for COVID-19 Fake News Detection and Fact Checking","date":"2020-11-26","arxiv_id":"2011.13253","n_code_links":1,"syntology":null},{"paper":null,"slug":"an-analysis-of-deep-object-detectors-for","title":"An Analysis of Deep Object Detectors For Diver Detection","date":"2020-11-25","arxiv_id":"2012.05701","n_code_links":0,"syntology":null},{"paper":"/paper/fast-object-segmentation-learning-with-kernel","slug":"fast-object-segmentation-learning-with-kernel","title":"Fast Object Segmentation Learning with Kernel-based Methods for Robotics","date":"2020-11-25","arxiv_id":"2011.12805","n_code_links":1,"syntology":null},{"paper":null,"slug":"fbwave-efficient-and-scalable-neural-vocoders","title":"FBWave: Efficient and Scalable Neural Vocoders for Streaming Text-To-Speech on the Edge","date":"2020-11-25","arxiv_id":"2011.12985","n_code_links":0,"syntology":null},{"paper":null,"slug":"learning-to-expand-reinforced-pseudo","title":"Learning to Expand: Reinforced Pseudo-relevance Feedback Selection for Information-seeking Conversations","date":"2020-11-25","arxiv_id":"2011.12771","n_code_links":0,"syntology":null},{"paper":null,"slug":"low-latency-cmos-hardware-acceleration-for","title":"Low Latency CMOS Hardware Acceleration for Fully Connected Layers in Deep Neural Networks","date":"2020-11-25","arxiv_id":"2011.12839","n_code_links":0,"syntology":null},{"paper":"/paper/neural-representations-for-modeling-variation","slug":"neural-representations-for-modeling-variation","title":"Neural Representations for Modeling Variation in Speech","date":"2020-11-25","arxiv_id":"2011.12649","n_code_links":1,"syntology":null},{"paper":null,"slug":"benchmarking-inference-performance-of-deep","title":"Benchmarking Inference Performance of Deep Learning Models on Analog Devices","date":"2020-11-24","arxiv_id":"2011.11840","n_code_links":0,"syntology":null},{"paper":"/paper/continuous-surface-embeddings-1","slug":"continuous-surface-embeddings-1","title":"Continuous Surface Embeddings","date":"2020-11-24","arxiv_id":"2011.12438","n_code_links":3,"syntology":{"ran":5,"of":6,"n_ran_checked":2,"n_instrument":3,"unverified":1,"pointer_only":0,"phrase":"5 ran (of which 1 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 0 violated, 1 with no contract checked; 3 where Syntology's instrument failed) · 1 unverified","official":{"repos":["facebookresearch/detectron2"],"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/enhancing-deep-neural-networks-with","slug":"enhancing-deep-neural-networks-with","title":"Enhancing deep neural networks with morphological information","date":"2020-11-24","arxiv_id":"2011.12432","n_code_links":2,"syntology":null},{"paper":null,"slug":"experiments-on-transfer-learning","title":"Experiments on transfer learning architectures for biomedical relation extraction","date":"2020-11-24","arxiv_id":"2011.12380","n_code_links":0,"syntology":null},{"paper":"/paper/glge-a-new-general-language-generation","slug":"glge-a-new-general-language-generation","title":"GLGE: A New General Language Generation Evaluation Benchmark","date":"2020-11-24","arxiv_id":"2011.11928","n_code_links":1,"syntology":null},{"paper":"/paper/picking-bert-s-brain-probing-for-linguistic","slug":"picking-bert-s-brain-probing-for-linguistic","title":"Picking BERT's Brain: Probing for Linguistic Dependencies in Contextualized Embeddings Using Representational Similarity Analysis","date":"2020-11-24","arxiv_id":"2011.12073","n_code_links":1,"syntology":{"ran":0,"of":1,"n_ran_checked":0,"n_instrument":0,"unverified":1,"pointer_only":0,"phrase":"0 ran · 1 unverified","official":{"repos":["mlepori1/Picking_BERTs_Brain"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"paper":"/paper/spinnet-learning-a-general-surface-descriptor","slug":"spinnet-learning-a-general-surface-descriptor","title":"SpinNet: Learning a General Surface Descriptor for 3D Point Cloud Registration","date":"2020-11-24","arxiv_id":"2011.12149","n_code_links":1,"syntology":{"ran":0,"of":1,"n_ran_checked":0,"n_instrument":0,"unverified":1,"pointer_only":0,"phrase":"0 ran · 1 unverified","official":{"repos":["QingyongHu/SpinNet"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"paper":"/paper/tfgan-time-and-frequency-domain-based","slug":"tfgan-time-and-frequency-domain-based","title":"TFGAN: Time and Frequency Domain Based Generative Adversarial Network for High-fidelity Speech Synthesis","date":"2020-11-24","arxiv_id":"2011.12206","n_code_links":1,"syntology":null},{"paper":"/paper/automated-quality-assessment-of-hand-washing","slug":"automated-quality-assessment-of-hand-washing","title":"Automated Quality Assessment of Hand Washing Using Deep Learning","date":"2020-11-23","arxiv_id":"2011.11383","n_code_links":1,"syntology":null},{"paper":null,"slug":"automatic-detection-and-classification-of-4","title":"Automatic Detection and Classification of Tick-borne Skin Lesions using Deep Learning","date":"2020-11-23","arxiv_id":"2011.11459","n_code_links":0,"syntology":null},{"paper":null,"slug":"bi-isca-bidirectional-inter-sentence","title":"Bi-ISCA: Bidirectional Inter-Sentence Contextual Attention Mechanism for Detecting Sarcasm in User Generated Noisy Short Text","date":"2020-11-23","arxiv_id":"2011.11465","n_code_links":0,"syntology":null},{"paper":null,"slug":"cancer-image-classification-based-on-densenet","title":"Cancer image classification based on DenseNet model","date":"2020-11-23","arxiv_id":"2011.11186","n_code_links":0,"syntology":null},{"paper":null,"slug":"deep-learning-for-automatic-quality-grading","title":"Deep Learning for Automatic Quality Grading of Mangoes: Methods and Insights","date":"2020-11-23","arxiv_id":"2011.11378","n_code_links":0,"syntology":null},{"paper":null,"slug":"detection-and-classification-of-mental","title":"Detection and Classification of mental illnesses on social media using RoBERTa","date":"2020-11-23","arxiv_id":"2011.11226","n_code_links":0,"syntology":null},{"paper":"/paper/does-bert-understand-sentiment-leveraging","slug":"does-bert-understand-sentiment-leveraging","title":"Does BERT Understand Sentiment? Leveraging Comparisons Between Contextual and Non-Contextual Embeddings to Improve Aspect-Based Sentiment Models","date":"2020-11-23","arxiv_id":"2011.11673","n_code_links":0,"syntology":null},{"paper":"/paper/effectiveness-of-mpc-friendly-softmax","slug":"effectiveness-of-mpc-friendly-softmax","title":"Effectiveness of MPC-friendly Softmax Replacement","date":"2020-11-23","arxiv_id":"2011.11202","n_code_links":3,"syntology":null},{"paper":"/paper/exploring-alternatives-to-softmax-function","slug":"exploring-alternatives-to-softmax-function","title":"Exploring Alternatives to Softmax Function","date":"2020-11-23","arxiv_id":"2011.11538","n_code_links":1,"syntology":null},{"paper":null,"slug":"imbalance-robust-softmax-for-deep-embeeding","title":"Imbalance Robust Softmax for Deep Embeeding Learning","date":"2020-11-23","arxiv_id":"2011.11155","n_code_links":0,"syntology":null},{"paper":"/paper/object-detection-neural-network-improves","slug":"object-detection-neural-network-improves","title":"Object detection neural network improves Fourier ptychography reconstruction","date":"2020-11-23","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/prior-to-segment-foreground-cues-for-novel","slug":"prior-to-segment-foreground-cues-for-novel","title":"Prior to Segment: Foreground Cues for Weakly Annotated Classes in Partially Supervised Instance Segmentation","date":"2020-11-23","arxiv_id":"2011.11787","n_code_links":1,"syntology":null},{"paper":null,"slug":"deep-learning-model-trained-on-mobile-phone","title":"Deep learning model trained on mobile phone-acquired frozen section images effectively detects basal cell carcinoma","date":"2020-11-22","arxiv_id":"2011.11081","n_code_links":0,"syntology":null},{"paper":null,"slug":"self-supervised-transformers-for-activity","title":"Self-Supervised Transformers for Activity Classification using Ambient Sensors","date":"2020-11-22","arxiv_id":"2011.12137","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-system-for-automatic-rice-disease","title":"A System for Automatic Rice Disease Detection from Rice Paddy Images Serviced via a Chatbot","date":"2020-11-21","arxiv_id":"2011.10823","n_code_links":0,"syntology":null},{"paper":"/paper/densely-connected-multidilated-convolutional","slug":"densely-connected-multidilated-convolutional","title":"Densely connected multidilated convolutional networks for dense prediction tasks","date":"2020-11-21","arxiv_id":"2011.11844","n_code_links":1,"syntology":null},{"paper":"/paper/rethinking-transformer-based-set-prediction","slug":"rethinking-transformer-based-set-prediction","title":"Rethinking Transformer-based Set Prediction for Object Detection","date":"2020-11-21","arxiv_id":"2011.10881","n_code_links":1,"syntology":{"ran":4,"of":4,"n_ran_checked":3,"n_instrument":1,"unverified":0,"pointer_only":0,"phrase":"4 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; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["edward-sun/tsp-detection"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/convtransformer-a-convolutional-transformer","slug":"convtransformer-a-convolutional-transformer","title":"ConvTransformer: A Convolutional Transformer Network for Video Frame Synthesis","date":"2020-11-20","arxiv_id":"2011.10185","n_code_links":2,"syntology":null},{"paper":"/paper/data-informed-global-sparseness-in-attention","slug":"data-informed-global-sparseness-in-attention","title":"Data-Informed Global Sparseness in Attention Mechanisms for Deep Neural Networks","date":"2020-11-20","arxiv_id":"2012.02030","n_code_links":2,"syntology":null},{"paper":"/paper/fine-tuning-bert-for-sentiment-analysis-of","slug":"fine-tuning-bert-for-sentiment-analysis-of","title":"Fine-Tuning BERT for Sentiment Analysis of Vietnamese Reviews","date":"2020-11-20","arxiv_id":"2011.10426","n_code_links":1,"syntology":null},{"paper":null,"slug":"large-scale-neural-architecture-search-with","title":"Large Scale Neural Architecture Search with Polyharmonic Splines","date":"2020-11-20","arxiv_id":"2011.10608","n_code_links":0,"syntology":null},{"paper":"/paper/multitask-learning-of-negation-and","slug":"multitask-learning-of-negation-and","title":"Multitask Learning of Negation and Speculation using Transformers","date":"2020-11-20","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/onion-a-simple-and-effective-defense-against","slug":"onion-a-simple-and-effective-defense-against","title":"ONION: A Simple and Effective Defense Against Textual Backdoor Attacks","date":"2020-11-20","arxiv_id":"2011.10369","n_code_links":2,"syntology":{"ran":3,"of":4,"n_ran_checked":0,"n_instrument":3,"unverified":1,"pointer_only":0,"phrase":"3 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; 3 where Syntology's instrument failed) · 1 unverified","official":{"repos":["thunlp/ONION"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":"/paper/recovering-the-imperfect-cell-segmentation-in","slug":"recovering-the-imperfect-cell-segmentation-in","title":"Recovering the Imperfect: Cell Segmentation in the Presence of Dynamically Localized Proteins","date":"2020-11-20","arxiv_id":"2011.10486","n_code_links":1,"syntology":null}],"record_sha256":"861cfc7c4ccf823ba5b826a7b672d29e29f65cbf11bacb44a07b4f4b80cc3ba1","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}