{"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/195","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":195,"pages_in_order":275,"rows_per_page":100,"rows":[19401,19500],"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/194","next":"/method/dropout/papers/196","papers":[{"paper":null,"slug":"towards-improving-topic-models-with-the-bert","title":"Towards Improving Topic Models with the BERT-based Neural Topic Encoder","date":"2021-11-16","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"understanding-attention-in-machine-reading-1","title":"Understanding Attention in Machine Reading Comprehension","date":"2021-11-16","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"unicon-unsupervised-intent-discovery-via","title":"UNICON: Unsupervised Intent Discovery via Semantic-level Contrastive Learning","date":"2021-11-16","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"unsupervised-multiple-choice-question","title":"Unsupervised multiple-choice question generation for out-of-domain Q\\&A fine-tuning","date":"2021-11-16","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"weight-squeezing-reparameterization-for-2","title":"Weight Squeezing: Reparameterization for Knowledge Transfer and Model Compression","date":"2021-11-16","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"wets-a-benchmark-for-translation-suggestion-1","title":"WeTS: A Benchmark for Translation Suggestion","date":"2021-11-16","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"what-works-and-doesn-t-work-a-deep-decoder","title":"What Works and Doesn't Work, A Deep Decoder for Neural Machine Translation","date":"2021-11-16","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"when-classifying-grammatical-role-bert-doesn","title":"When classifying grammatical role, BERT doesn't care about word order... except when it matters","date":"2021-11-16","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"ai-in-games-techniques-challenges-and","title":"AI in Human-computer Gaming: Techniques, Challenges and Opportunities","date":"2021-11-15","arxiv_id":"2111.07631","n_code_links":0,"syntology":null},{"paper":null,"slug":"assessing-gender-bias-in-medical-and","title":"Assessing gender bias in medical and scientific masked language models with StereoSet","date":"2021-11-15","arxiv_id":"2111.08088","n_code_links":0,"syntology":null},{"paper":"/paper/automated-audio-captioning-by-fine-tuning","slug":"automated-audio-captioning-by-fine-tuning","title":"AUTOMATED AUDIO CAPTIONING BY FINE-TUNING BART WITH AUDIOSET TAGS","date":"2021-11-15","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"calculating-question-similarity-is-enough-a","title":"Calculating Question Similarity is Enough: A New Method for KBQA Tasks","date":"2021-11-15","arxiv_id":"2111.07658","n_code_links":0,"syntology":null},{"paper":null,"slug":"energy-optimal-design-and-control-of-electric-1","title":"Energy-optimal Design and Control of Electric Powertrains under Motor Thermal Constraints","date":"2021-11-15","arxiv_id":"2111.07711","n_code_links":0,"syntology":null},{"paper":null,"slug":"exploring-story-generation-with-multi-task","title":"Exploring Story Generation with Multi-task Objectives in Variational Autoencoders","date":"2021-11-15","arxiv_id":"2111.08133","n_code_links":0,"syntology":null},{"paper":null,"slug":"faketransformer-exposing-face-forgery-from","title":"FakeTransformer: Exposing Face Forgery From Spatial-Temporal Representation Modeled By Facial Pixel Variations","date":"2021-11-15","arxiv_id":"2111.07601","n_code_links":0,"syntology":null},{"paper":"/paper/iiitt-dravidian-codemix-fire2021","slug":"iiitt-dravidian-codemix-fire2021","title":"IIITT@Dravidian-CodeMix-FIRE2021: Transliterate or translate? Sentiment analysis of code-mixed text in Dravidian languages","date":"2021-11-15","arxiv_id":"2111.07906","n_code_links":1,"syntology":null},{"paper":null,"slug":"improving-prosody-for-unseen-texts-in-speech","title":"Improving Prosody for Unseen Texts in Speech Synthesis by Utilizing Linguistic Information and Noisy Data","date":"2021-11-15","arxiv_id":"2111.07549","n_code_links":0,"syntology":null},{"paper":"/paper/mask-guided-spectral-wise-transformer-for","slug":"mask-guided-spectral-wise-transformer-for","title":"Mask-guided Spectral-wise Transformer for Efficient Hyperspectral Image Reconstruction","date":"2021-11-15","arxiv_id":"2111.07910","n_code_links":4,"syntology":{"ran":3,"of":3,"n_ran_checked":0,"n_instrument":3,"unverified":0,"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) · 0 unverified","official":{"repos":["caiyuanhao1998/MST"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"say-what-collaborative-pop-lyric-generation","title":"Say What? Collaborative Pop Lyric Generation Using Multitask Transfer Learning","date":"2021-11-15","arxiv_id":"2111.07592","n_code_links":0,"syntology":null},{"paper":null,"slug":"scaling-law-for-recommendation-models-towards","title":"Scaling Law for Recommendation Models: Towards General-purpose User Representations","date":"2021-11-15","arxiv_id":"2111.11294","n_code_links":0,"syntology":null},{"paper":"/paper/local-multi-head-channel-self-attention-for","slug":"local-multi-head-channel-self-attention-for","title":"Local Multi-Head Channel Self-Attention for Facial Expression Recognition","date":"2021-11-14","arxiv_id":"2111.07224","n_code_links":1,"syntology":null},{"paper":null,"slug":"will-you-find-these-shortcuts-a-protocol-for","title":"\"Will You Find These Shortcuts?\" A Protocol for Evaluating the Faithfulness of Input Salience Methods for Text Classification","date":"2021-11-14","arxiv_id":"2111.07367","n_code_links":0,"syntology":null},{"paper":null,"slug":"factorial-convolution-neural-networks","title":"Factorial Convolution Neural Networks","date":"2021-11-13","arxiv_id":"2111.07072","n_code_links":0,"syntology":null},{"paper":null,"slug":"mc-cim-compute-in-memory-with-monte-carlo","title":"MC-CIM: Compute-in-Memory with Monte-Carlo Dropouts for Bayesian Edge Intelligence","date":"2021-11-13","arxiv_id":"2111.07125","n_code_links":0,"syntology":null},{"paper":null,"slug":"socialbert-transformers-for-online","title":"SocialBERT -- Transformers for Online SocialNetwork Language Modelling","date":"2021-11-13","arxiv_id":"2111.07148","n_code_links":0,"syntology":null},{"paper":"/paper/monte-carlo-dropout-increases-model","slug":"monte-carlo-dropout-increases-model","title":"Monte Carlo dropout increases model repeatability","date":"2021-11-12","arxiv_id":"2111.06754","n_code_links":1,"syntology":null},{"paper":"/paper/ms-latte-a-dataset-of-where-and-when-to-do","slug":"ms-latte-a-dataset-of-where-and-when-to-do","title":"MS-LaTTE: A Dataset of Where and When To-do Tasks are Completed","date":"2021-11-12","arxiv_id":"2111.06902","n_code_links":1,"syntology":null},{"paper":null,"slug":"the-channel-spatial-attention-based-vision","title":"The self-supervised spectral-spatial attention-based transformer network for automated, accurate prediction of crop nitrogen status from UAV imagery","date":"2021-11-12","arxiv_id":"2111.06839","n_code_links":0,"syntology":null},{"paper":null,"slug":"transformer-based-image-compression","title":"Transformer-based Image Compression","date":"2021-11-12","arxiv_id":"2111.06707","n_code_links":0,"syntology":null},{"paper":"/paper/a-survey-of-visual-transformers","slug":"a-survey-of-visual-transformers","title":"A Survey of Visual Transformers","date":"2021-11-11","arxiv_id":"2111.06091","n_code_links":1,"syntology":null},{"paper":"/paper/automated-question-generation-and-question","slug":"automated-question-generation-and-question","title":"Automated question generation and question answering from Turkish texts","date":"2021-11-11","arxiv_id":"2111.06476","n_code_links":1,"syntology":null},{"paper":"/paper/character-level-hypernetworks-for-hate-speech","slug":"character-level-hypernetworks-for-hate-speech","title":"Character-level HyperNetworks for Hate Speech Detection","date":"2021-11-11","arxiv_id":"2111.06336","n_code_links":1,"syntology":null},{"paper":"/paper/dropgnn-random-dropouts-increase-the","slug":"dropgnn-random-dropouts-increase-the","title":"DropGNN: Random Dropouts Increase the Expressiveness of Graph Neural Networks","date":"2021-11-11","arxiv_id":"2111.06283","n_code_links":1,"syntology":{"ran":2,"of":3,"n_ran_checked":2,"n_instrument":0,"unverified":1,"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) · 1 unverified","official":{"repos":["karolismart/dropgnn"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"graph-relation-transformer-incorporating","title":"Graph Relation Transformer: Incorporating pairwise object features into the Transformer architecture","date":"2021-11-11","arxiv_id":"2111.06075","n_code_links":0,"syntology":null},{"paper":null,"slug":"improving-large-scale-language-models-and","title":"Improving Large-scale Language Models and Resources for Filipino","date":"2021-11-11","arxiv_id":"2111.06053","n_code_links":0,"syntology":null},{"paper":null,"slug":"on-efficient-uncertainty-estimation-for","title":"On Efficient Uncertainty Estimation for Resource-Constrained Mobile Applications","date":"2021-11-11","arxiv_id":"2111.09838","n_code_links":0,"syntology":null},{"paper":"/paper/a-novel-corpus-of-discourse-structure-in","slug":"a-novel-corpus-of-discourse-structure-in","title":"A Novel Corpus of Discourse Structure in Humans and Computers","date":"2021-11-10","arxiv_id":"2111.05940","n_code_links":1,"syntology":null},{"paper":null,"slug":"amazon-sagemaker-model-parallelism-a-general","title":"Amazon SageMaker Model Parallelism: A General and Flexible Framework for Large Model Training","date":"2021-11-10","arxiv_id":"2111.05972","n_code_links":0,"syntology":null},{"paper":"/paper/attention-approximates-sparse-distributed","slug":"attention-approximates-sparse-distributed","title":"Attention Approximates Sparse Distributed Memory","date":"2021-11-10","arxiv_id":"2111.05498","n_code_links":1,"syntology":{"ran":12,"of":19,"n_ran_checked":11,"n_instrument":1,"unverified":7,"pointer_only":7,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 0 honoured, 0 violated, 11 with no contract checked; 1 where Syntology's instrument failed) · 7 unverified","official":{"repos":["trentbrick/attention-approximates-sdm"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":11,"n_unverified":7,"ran_from_kinds":["official"]}}},{"paper":"/paper/bagbert-bert-based-bagging-stacking-for-multi","slug":"bagbert-bert-based-bagging-stacking-for-multi","title":"BagBERT: BERT-based bagging-stacking for multi-topic classification","date":"2021-11-10","arxiv_id":"2111.05808","n_code_links":1,"syntology":null},{"paper":null,"slug":"cehr-bert-incorporating-temporal-information","title":"CEHR-BERT: Incorporating temporal information from structured EHR data to improve prediction tasks","date":"2021-11-10","arxiv_id":"2111.08585","n_code_links":0,"syntology":null},{"paper":"/paper/multimodal-transformer-with-variable-length","slug":"multimodal-transformer-with-variable-length","title":"Multimodal Transformer with Variable-length Memory for Vision-and-Language Navigation","date":"2021-11-10","arxiv_id":"2111.05759","n_code_links":1,"syntology":null},{"paper":"/paper/prune-once-for-all-sparse-pre-trained","slug":"prune-once-for-all-sparse-pre-trained","title":"Prune Once for All: Sparse Pre-Trained Language Models","date":"2021-11-10","arxiv_id":"2111.05754","n_code_links":2,"syntology":null},{"paper":"/paper/soft-sensing-transformer-hundreds-of-sensors","slug":"soft-sensing-transformer-hundreds-of-sensors","title":"Soft Sensing Transformer: Hundreds of Sensors are Worth a Single Word","date":"2021-11-10","arxiv_id":"2111.05973","n_code_links":1,"syntology":null},{"paper":"/paper/convolutional-neural-network-dynamics-a-graph-1","slug":"convolutional-neural-network-dynamics-a-graph-1","title":"Leveraging the Graph Structure of Neural Network Training Dynamics","date":"2021-11-09","arxiv_id":"2111.05410","n_code_links":1,"syntology":null},{"paper":null,"slug":"distir-an-intermediate-representation-and","title":"DistIR: An Intermediate Representation and Simulator for Efficient Neural Network Distribution","date":"2021-11-09","arxiv_id":"2111.05426","n_code_links":0,"syntology":null},{"paper":null,"slug":"dsbert-unsupervised-dialogue-structure","title":"DSBERT:Unsupervised Dialogue Structure learning with BERT","date":"2021-11-09","arxiv_id":"2111.04933","n_code_links":0,"syntology":null},{"paper":null,"slug":"fpm-a-collection-of-large-scale-foundation","title":"FPM: A Collection of Large-scale Foundation Pre-trained Language Models","date":"2021-11-09","arxiv_id":"2111.04909","n_code_links":0,"syntology":null},{"paper":null,"slug":"human-in-the-loop-disinformation-detection","title":"Human-in-the-Loop Disinformation Detection: Stance, Sentiment, or Something Else?","date":"2021-11-09","arxiv_id":"2111.05139","n_code_links":0,"syntology":null},{"paper":null,"slug":"multi-task-prediction-of-clinical-outcomes-in","title":"Multi-Task Prediction of Clinical Outcomes in the Intensive Care Unit using Flexible Multimodal Transformers","date":"2021-11-09","arxiv_id":"2111.05431","n_code_links":0,"syntology":null},{"paper":"/paper/sliced-recursive-transformer-1","slug":"sliced-recursive-transformer-1","title":"Sliced Recursive Transformer","date":"2021-11-09","arxiv_id":"2111.05297","n_code_links":1,"syntology":null},{"paper":"/paper/a-comparison-of-deep-learning-architectures","slug":"a-comparison-of-deep-learning-architectures","title":"A Comparison of Deep Learning Architectures for Optical Galaxy Morphology Classification","date":"2021-11-08","arxiv_id":"2111.04353","n_code_links":2,"syntology":null},{"paper":"/paper/ai-upv-at-iberlef-2021-detoxis-task-toxicity","slug":"ai-upv-at-iberlef-2021-detoxis-task-toxicity","title":"AI-UPV at IberLEF-2021 DETOXIS task: Toxicity Detection in Immigration-Related Web News Comments Using Transformers and Statistical Models","date":"2021-11-08","arxiv_id":"2111.04530","n_code_links":1,"syntology":null},{"paper":"/paper/chemical-detection-and-indexing-in-pubmed","slug":"chemical-detection-and-indexing-in-pubmed","title":"Chemical detection and indexing in PubMed full text articles using deep learning and rule-based methods","date":"2021-11-08","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"detecting-depression-in-thai-blog-posts-a-1","title":"Detecting Depression in Thai Blog Posts: a Dataset and a Baseline","date":"2021-11-08","arxiv_id":"2111.04574","n_code_links":0,"syntology":null},{"paper":"/paper/fast-and-scalable-spike-and-slab-variable","slug":"fast-and-scalable-spike-and-slab-variable","title":"Fast and Scalable Spike and Slab Variable Selection in High-Dimensional Gaussian Processes","date":"2021-11-08","arxiv_id":"2111.04558","n_code_links":1,"syntology":null},{"paper":"/paper/good-robot-now-watch-this-repurposing","slug":"good-robot-now-watch-this-repurposing","title":"\"Good Robot! Now Watch This!\": Repurposing Reinforcement Learning for Task-to-Task Transfer","date":"2021-11-08","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/guiding-multi-step-rearrangement-tasks-with","slug":"guiding-multi-step-rearrangement-tasks-with","title":"Guiding Multi-Step Rearrangement Tasks with Natural Language Instructions","date":"2021-11-08","arxiv_id":null,"n_code_links":2,"syntology":null},{"paper":"/paper/joint-inference-for-neural-network-depth-and","slug":"joint-inference-for-neural-network-depth-and","title":"Joint Inference for Neural Network Depth and Dropout Regularization","date":"2021-11-08","arxiv_id":null,"n_code_links":2,"syntology":null},{"paper":"/paper/mixed-transformer-u-net-for-medical-image","slug":"mixed-transformer-u-net-for-medical-image","title":"Mixed Transformer U-Net For Medical Image Segmentation","date":"2021-11-08","arxiv_id":"2111.04734","n_code_links":1,"syntology":{"ran":2,"of":2,"n_ran_checked":2,"n_instrument":0,"unverified":0,"pointer_only":2,"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":["dootmaan/mt-unet"],"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":"/paper/sexism-prediction-in-spanish-and-english","slug":"sexism-prediction-in-spanish-and-english","title":"Sexism Prediction in Spanish and English Tweets Using Monolingual and Multilingual BERT and Ensemble Models","date":"2021-11-08","arxiv_id":"2111.04551","n_code_links":1,"syntology":null},{"paper":"/paper/synthesizing-collective-communication","slug":"synthesizing-collective-communication","title":"TACCL: Guiding Collective Algorithm Synthesis using Communication Sketches","date":"2021-11-08","arxiv_id":"2111.04867","n_code_links":2,"syntology":null},{"paper":"/paper/are-we-ready-for-a-new-paradigm-shift-a","slug":"are-we-ready-for-a-new-paradigm-shift-a","title":"Are we ready for a new paradigm shift? A Survey on Visual Deep MLP","date":"2021-11-07","arxiv_id":"2111.04060","n_code_links":1,"syntology":null},{"paper":"/paper/tacl-improving-bert-pre-training-with-token","slug":"tacl-improving-bert-pre-training-with-token","title":"TaCL: Improving BERT Pre-training with Token-aware Contrastive Learning","date":"2021-11-07","arxiv_id":"2111.04198","n_code_links":2,"syntology":null},{"paper":"/paper/texture-enhanced-light-field-super-resolution","slug":"texture-enhanced-light-field-super-resolution","title":"Texture-enhanced Light Field Super-resolution with Spatio-Angular Decomposition Kernels","date":"2021-11-07","arxiv_id":"2111.04069","n_code_links":1,"syntology":null},{"paper":null,"slug":"analyzing-architectures-for-neural-machine","title":"Analyzing Architectures for Neural Machine Translation Using Low Computational Resources","date":"2021-11-06","arxiv_id":"2111.03813","n_code_links":0,"syntology":null},{"paper":"/paper/benchmarking-data-driven-surrogate-simulators","slug":"benchmarking-data-driven-surrogate-simulators","title":"Benchmarking Data-driven Surrogate Simulators for Artificial Electromagnetic Materials","date":"2021-11-06","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"convolutional-gated-mlp-combining","title":"Convolutional Gated MLP: Combining Convolutions & gMLP","date":"2021-11-06","arxiv_id":"2111.03940","n_code_links":0,"syntology":null},{"paper":null,"slug":"profitable-trade-off-between-memory-and","title":"Profitable Trade-Off Between Memory and Performance In Multi-Domain Chatbot Architectures","date":"2021-11-06","arxiv_id":"2111.03963","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-deep-learning-generative-model-approach-for","title":"A Deep Learning Generative Model Approach for Image Synthesis of Plant Leaves","date":"2021-11-05","arxiv_id":"2111.03388","n_code_links":0,"syntology":null},{"paper":null,"slug":"context-aware-transformer-transducer-for","title":"Context-Aware Transformer Transducer for Speech Recognition","date":"2021-11-05","arxiv_id":"2111.03250","n_code_links":0,"syntology":null},{"paper":null,"slug":"conversational-speech-recognition-leveraging","title":"Effective Cross-Utterance Language Modeling for Conversational Speech Recognition","date":"2021-11-05","arxiv_id":"2111.03333","n_code_links":0,"syntology":null},{"paper":null,"slug":"fbnet-feature-balance-network-for-urban-scene","title":"FBNet: Feature Balance Network for Urban-Scene Segmentation","date":"2021-11-05","arxiv_id":"2111.03286","n_code_links":0,"syntology":null},{"paper":null,"slug":"fighting-covid-19-in-the-dark-methodology-for","title":"A methodology for training homomorphicencryption friendly neural networks","date":"2021-11-05","arxiv_id":"2111.03362","n_code_links":0,"syntology":null},{"paper":null,"slug":"ibert-idiom-cloze-style-reading-comprehension","title":"IBERT: Idiom Cloze-style reading comprehension with Attention","date":"2021-11-05","arxiv_id":"2112.02994","n_code_links":0,"syntology":null},{"paper":null,"slug":"improving-visual-quality-of-image-synthesis","title":"Improving Visual Quality of Image Synthesis by A Token-based Generator with Transformers","date":"2021-11-05","arxiv_id":"2111.03481","n_code_links":0,"syntology":null},{"paper":null,"slug":"oracle-teacher-towards-better-knowledge","title":"Oracle Teacher: Leveraging Target Information for Better Knowledge Distillation of CTC Models","date":"2021-11-05","arxiv_id":"2111.03664","n_code_links":0,"syntology":null},{"paper":null,"slug":"pathological-analysis-of-blood-cells-using","title":"Pathological Analysis of Blood Cells Using Deep Learning Techniques","date":"2021-11-05","arxiv_id":"2111.03274","n_code_links":0,"syntology":null},{"paper":null,"slug":"sexism-identification-in-tweets-and-gabs","title":"Sexism Identification in Tweets and Gabs using Deep Neural Networks","date":"2021-11-05","arxiv_id":"2111.03612","n_code_links":0,"syntology":null},{"paper":"/paper/solving-traffic4cast-competition-with-u-net","slug":"solving-traffic4cast-competition-with-u-net","title":"Solving Traffic4Cast Competition with U-Net and Temporal Domain Adaptation","date":"2021-11-05","arxiv_id":"2111.03421","n_code_links":1,"syntology":{"ran":5,"of":5,"n_ran_checked":5,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["jbr-ai-labs/traffic4cast-2021"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/taskdrop-a-competitive-baseline-for-continual","slug":"taskdrop-a-competitive-baseline-for-continual","title":"TaskDrop: A Competitive Baseline for Continual Learning of Sentiment Classification","date":"2021-11-05","arxiv_id":"2112.02995","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-text-autoencoder-from-transformer-for-fast","title":"A text autoencoder from transformer for fast encoding language representation","date":"2021-11-04","arxiv_id":"2111.02844","n_code_links":0,"syntology":null},{"paper":"/paper/an-empirical-study-of-the-effectiveness-of-an","slug":"an-empirical-study-of-the-effectiveness-of-an","title":"An Empirical Study of the Effectiveness of an Ensemble of Stand-alone Sentiment Detection Tools for Software Engineering Datasets","date":"2021-11-04","arxiv_id":"2111.03196","n_code_links":1,"syntology":null},{"paper":"/paper/benchmarking-multimodal-automl-for-tabular","slug":"benchmarking-multimodal-automl-for-tabular","title":"Benchmarking Multimodal AutoML for Tabular Data with Text Fields","date":"2021-11-04","arxiv_id":"2111.02705","n_code_links":2,"syntology":{"ran":2,"of":2,"n_ran_checked":0,"n_instrument":2,"unverified":0,"pointer_only":2,"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) · 0 unverified","official":{"repos":["sxjscience/automl_multimodal_benchmark","awslabs/autogluon"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/conformal-prediction-for-text-infilling-and","slug":"conformal-prediction-for-text-infilling-and","title":"Conformal prediction for text infilling and part-of-speech prediction","date":"2021-11-04","arxiv_id":"2111.02592","n_code_links":1,"syntology":null},{"paper":"/paper/mt3-multi-task-multitrack-music-transcription-1","slug":"mt3-multi-task-multitrack-music-transcription-1","title":"MT3: Multi-Task Multitrack Music Transcription","date":"2021-11-04","arxiv_id":"2111.03017","n_code_links":3,"syntology":{"ran":0,"of":6,"n_ran_checked":0,"n_instrument":0,"unverified":6,"pointer_only":0,"phrase":"0 ran · 6 unverified","official":{"repos":["magenta/mt3"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":6,"ran_from_kinds":[]}}},{"paper":null,"slug":"multi-airport-delay-prediction-with","title":"Multi-Airport Delay Prediction with Transformers","date":"2021-11-04","arxiv_id":"2111.04494","n_code_links":0,"syntology":null},{"paper":"/paper/an-empirical-study-of-training-end-to-end","slug":"an-empirical-study-of-training-end-to-end","title":"An Empirical Study of Training End-to-End Vision-and-Language Transformers","date":"2021-11-03","arxiv_id":"2111.02387","n_code_links":3,"syntology":{"ran":1,"of":1,"n_ran_checked":0,"n_instrument":1,"unverified":0,"pointer_only":0,"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":["zdou0830/meter"],"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/an-explanation-of-in-context-learning-as-1","slug":"an-explanation-of-in-context-learning-as-1","title":"An Explanation of In-context Learning as Implicit Bayesian Inference","date":"2021-11-03","arxiv_id":"2111.02080","n_code_links":1,"syntology":{"ran":3,"of":3,"n_ran_checked":2,"n_instrument":1,"unverified":0,"pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["p-lambda/incontext-learning"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"bert-dre-bert-with-deep-recursive-encoder-for","title":"BERT-DRE: BERT with Deep Recursive Encoder for Natural Language Sentence Matching","date":"2021-11-03","arxiv_id":"2111.02188","n_code_links":0,"syntology":null},{"paper":null,"slug":"prostformer-pre-trained-progressive-space","title":"ProSTformer: Pre-trained Progressive Space-Time Self-attention Model for Traffic Flow Forecasting","date":"2021-11-03","arxiv_id":"2111.03459","n_code_links":0,"syntology":null},{"paper":null,"slug":"rethinking-the-image-feature-biases-exhibited","title":"Rethinking the Image Feature Biases Exhibited by Deep CNN Models","date":"2021-11-03","arxiv_id":"2111.02058","n_code_links":0,"syntology":null},{"paper":null,"slug":"theeyecorpus-experiments-in-reducing-nlp-bias","title":"TheEyeCorpus: Experiments in Reducing NLP Bias and Identifiability for Large LMs","date":"2021-11-03","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/vlmo-unified-vision-language-pre-training","slug":"vlmo-unified-vision-language-pre-training","title":"VLMo: Unified Vision-Language Pre-Training with Mixture-of-Modality-Experts","date":"2021-11-03","arxiv_id":"2111.02358","n_code_links":2,"syntology":null},{"paper":null,"slug":"can-vision-transformers-perform-convolution-1","title":"Can Vision Transformers Perform Convolution?","date":"2021-11-02","arxiv_id":"2111.01353","n_code_links":0,"syntology":null},{"paper":null,"slug":"detection-of-hate-speech-using-bert-and-hate","title":"Detection of Hate Speech using BERT and Hate Speech Word Embedding with Deep Model","date":"2021-11-02","arxiv_id":"2111.01515","n_code_links":0,"syntology":null},{"paper":null,"slug":"explaining-documents-relevance-to-search","title":"Explaining Documents' Relevance to Search Queries","date":"2021-11-02","arxiv_id":"2111.01314","n_code_links":0,"syntology":null},{"paper":null,"slug":"federated-split-vision-transformer-for-covid","title":"Federated Split Vision Transformer for COVID-19 CXR Diagnosis using Task-Agnostic Training","date":"2021-11-02","arxiv_id":"2111.01338","n_code_links":0,"syntology":null},{"paper":null,"slug":"low-rank-sparse-tensor-compression-for-neural","title":"Low-Rank+Sparse Tensor Compression for Neural Networks","date":"2021-11-02","arxiv_id":"2111.01697","n_code_links":0,"syntology":null},{"paper":"/paper/relational-self-attention-what-s-missing-in","slug":"relational-self-attention-what-s-missing-in","title":"Relational Self-Attention: What's Missing in Attention for Video Understanding","date":"2021-11-02","arxiv_id":"2111.01673","n_code_links":1,"syntology":{"ran":1,"of":1,"n_ran_checked":1,"n_instrument":0,"unverified":0,"pointer_only":1,"phrase":"1 ran (of which 1 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; the one sample that ran constructed an object rather than computing a result","official":{"repos":["KimManjin/RSA"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}}],"record_sha256":"0f56a4ef1adbbf073146c8f2676fd6036ee7291934cac45ae9975329f03900c2","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}