{"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/linear-layer/papers/205","list_of":"/method/linear-layer","method":"Linear Layer","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":205,"pages_in_order":255,"rows_per_page":100,"rows":[20401,20500],"of":25421,"counts":{"archive_papers_tagged":25421,"with_a_code_link":11479,"where_syntology_ran_a_sample":3523,"not_listed_spam_title":0,"listed":25421,"listed_where_code_ran":3523,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":2976,"every_run_a_failure_of_syntologys_instrument":547,"listed_with_a_run_with_no_instrument_failure":2976,"listed_every_run_a_failure_of_syntologys_instrument":547,"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/linear-layer","prev":"/method/linear-layer/papers/204","next":"/method/linear-layer/papers/206","papers":[{"paper":"/paper/learning-attributed-graph-representations","slug":"learning-attributed-graph-representations","title":"Learning Attributed Graph Representations with Communicative Message Passing Transformer","date":"2021-07-19","arxiv_id":"2107.08773","n_code_links":1,"syntology":null},{"paper":"/paper/levit-unet-make-faster-encoders-with","slug":"levit-unet-make-faster-encoders-with","title":"LeViT-UNet: Make Faster Encoders with Transformer for Medical Image Segmentation","date":"2021-07-19","arxiv_id":"2107.08623","n_code_links":2,"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":["apple1986/LeViT_UNet"],"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/long-term-series-forecasting-with-query","slug":"long-term-series-forecasting-with-query","title":"Long-term series forecasting with Query Selector -- efficient model of sparse attention","date":"2021-07-19","arxiv_id":"2107.08687","n_code_links":2,"syntology":null},{"paper":null,"slug":"residual-tree-aggregation-of-layers-for","title":"Residual Tree Aggregation of Layers for Neural Machine Translation","date":"2021-07-19","arxiv_id":"2107.14590","n_code_links":0,"syntology":null},{"paper":"/paper/sequence-to-sequence-piano-transcription-with","slug":"sequence-to-sequence-piano-transcription-with","title":"Sequence-to-Sequence Piano Transcription with Transformers","date":"2021-07-19","arxiv_id":"2107.09142","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":null}},{"paper":"/paper/unik-a-unified-framework-for-real-world","slug":"unik-a-unified-framework-for-real-world","title":"UNIK: A Unified Framework for Real-world Skeleton-based Action Recognition","date":"2021-07-19","arxiv_id":"2107.08580","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-discriminative-semantic-ranker-for-question","title":"A Discriminative Semantic Ranker for Question Retrieval","date":"2021-07-18","arxiv_id":"2107.08345","n_code_links":0,"syntology":null},{"paper":"/paper/aspect-based-sentiment-analysis-using-bert","slug":"aspect-based-sentiment-analysis-using-bert","title":"Aspect-based Sentiment Analysis using BERT with Disentangled Attention","date":"2021-07-18","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"stock-price-prediction-using-bert-and-gan","title":"Stock price prediction using BERT and GAN","date":"2021-07-18","arxiv_id":"2107.09055","n_code_links":0,"syntology":null},{"paper":"/paper/tfix-learning-to-fix-coding-errors-with-a","slug":"tfix-learning-to-fix-coding-errors-with-a","title":"TFix: Learning to Fix Coding Errors with a Text-to-Text Transformer","date":"2021-07-18","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"a-vector-based-approach-to-few-shot-veracity","title":"A Vector-Based Approach to Few-Shot Veracity Classification for Automated Fact-Checking","date":"2021-07-17","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"dynamic-transformer-for-efficient-machine","title":"Dynamic Transformer for Efficient Machine Translation on Embedded Devices","date":"2021-07-17","arxiv_id":"2107.08199","n_code_links":0,"syntology":null},{"paper":null,"slug":"neural-search-learning-query-and-product","title":"Neural Search: Learning Query and Product Representations in Fashion E-commerce","date":"2021-07-17","arxiv_id":"2107.08291","n_code_links":0,"syntology":null},{"paper":"/paper/rams-trans-recurrent-attention-multi-scale","slug":"rams-trans-recurrent-attention-multi-scale","title":"RAMS-Trans: Recurrent Attention Multi-scale Transformer forFine-grained Image Recognition","date":"2021-07-17","arxiv_id":"2107.08192","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-comparison-of-deep-learning-classification","title":"A Comparative Study of Deep Learning Classification Methods on a Small Environmental Microorganism Image Dataset (EMDS-6): from Convolutional Neural Networks to Visual Transformers","date":"2021-07-16","arxiv_id":"2107.07699","n_code_links":0,"syntology":null},{"paper":"/paper/is-attention-to-bounding-boxes-all-you-need","slug":"is-attention-to-bounding-boxes-all-you-need","title":"Is attention to bounding boxes all you need for pedestrian action prediction?","date":"2021-07-16","arxiv_id":"2107.08031","n_code_links":0,"syntology":null},{"paper":null,"slug":"the-law-of-large-documents-understanding-the","title":"The Law of Large Documents: Understanding the Structure of Legal Contracts Using Visual Cues","date":"2021-07-16","arxiv_id":"2107.08128","n_code_links":0,"syntology":null},{"paper":"/paper/autobert-zero-evolving-bert-backbone-from","slug":"autobert-zero-evolving-bert-backbone-from","title":"AutoBERT-Zero: Evolving BERT Backbone from Scratch","date":"2021-07-15","arxiv_id":"2107.07445","n_code_links":0,"syntology":null},{"paper":"/paper/automatic-task-requirements-writing","slug":"automatic-task-requirements-writing","title":"Automatic Task Requirements Writing Evaluation via Machine Reading Comprehension","date":"2021-07-15","arxiv_id":"2107.07957","n_code_links":1,"syntology":null},{"paper":"/paper/fewclue-a-chinese-few-shot-learning","slug":"fewclue-a-chinese-few-shot-learning","title":"FewCLUE: A Chinese Few-shot Learning Evaluation Benchmark","date":"2021-07-15","arxiv_id":"2107.07498","n_code_links":1,"syntology":null},{"paper":"/paper/learning-sparse-interaction-graphs-of","slug":"learning-sparse-interaction-graphs-of","title":"Learning Sparse Interaction Graphs of Partially Detected Pedestrians for Trajectory Prediction","date":"2021-07-15","arxiv_id":"2107.07056","n_code_links":1,"syntology":null},{"paper":"/paper/only-train-once-a-one-shot-neural-network","slug":"only-train-once-a-one-shot-neural-network","title":"Only Train Once: A One-Shot Neural Network Training And Pruning Framework","date":"2021-07-15","arxiv_id":"2107.07467","n_code_links":1,"syntology":null},{"paper":"/paper/self-supervised-contrastive-learning-with","slug":"self-supervised-contrastive-learning-with","title":"Self-Supervised Contrastive Learning with Adversarial Perturbations for Defending Word Substitution-based Attacks","date":"2021-07-15","arxiv_id":"2107.07610","n_code_links":1,"syntology":null},{"paper":"/paper/star-sparse-transformer-based-action","slug":"star-sparse-transformer-based-action","title":"STAR: Sparse Transformer-based Action Recognition","date":"2021-07-15","arxiv_id":"2107.07089","n_code_links":1,"syntology":null},{"paper":"/paper/transformer-based-machine-learning-for-fast","slug":"transformer-based-machine-learning-for-fast","title":"Transformer-based Machine Learning for Fast SAT Solvers and Logic Synthesis","date":"2021-07-15","arxiv_id":"2107.07116","n_code_links":1,"syntology":null},{"paper":"/paper/trusting-roberta-over-bert-insights-from","slug":"trusting-roberta-over-bert-insights-from","title":"Trusting RoBERTa over BERT: Insights from CheckListing the Natural Language Inference Task","date":"2021-07-15","arxiv_id":"2107.07229","n_code_links":1,"syntology":null},{"paper":"/paper/turning-tables-generating-examples-from-semi","slug":"turning-tables-generating-examples-from-semi","title":"Turning Tables: Generating Examples from Semi-structured Tables for Endowing Language Models with Reasoning Skills","date":"2021-07-15","arxiv_id":"2107.07261","n_code_links":1,"syntology":null},{"paper":"/paper/a-note-on-learning-rare-events-in-molecular","slug":"a-note-on-learning-rare-events-in-molecular","title":"A Note on Learning Rare Events in Molecular Dynamics using LSTM and Transformer","date":"2021-07-14","arxiv_id":"2107.06573","n_code_links":1,"syntology":null},{"paper":null,"slug":"bert-fine-tuning-for-sentiment-analysis-on","title":"BERT Fine-Tuning for Sentiment Analysis on Indonesian Mobile Apps Reviews","date":"2021-07-14","arxiv_id":"2107.06802","n_code_links":0,"syntology":null},{"paper":"/paper/chimera-efficiently-training-large-scale","slug":"chimera-efficiently-training-large-scale","title":"Chimera: Efficiently Training Large-Scale Neural Networks with Bidirectional Pipelines","date":"2021-07-14","arxiv_id":"2107.06925","n_code_links":1,"syntology":null},{"paper":null,"slug":"from-carbon-transition-premium-to-carbon","title":"Single Event Transition Risk: A Measure for Long Term Carbon Exposure","date":"2021-07-14","arxiv_id":"2107.06518","n_code_links":0,"syntology":null},{"paper":"/paper/indonesia-s-fake-news-detection-using","slug":"indonesia-s-fake-news-detection-using","title":"Indonesia's Fake News Detection using Transformer Network","date":"2021-07-14","arxiv_id":"2107.06796","n_code_links":1,"syntology":null},{"paper":null,"slug":"large-scale-news-classification-using-bert","title":"Large-Scale News Classification using BERT Language Model: Spark NLP Approach","date":"2021-07-14","arxiv_id":"2107.06785","n_code_links":0,"syntology":null},{"paper":"/paper/scalable-memory-protection-in-the-penglai","slug":"scalable-memory-protection-in-the-penglai","title":"Scalable Memory Protection in the PENGLAI Enclave","date":"2021-07-14","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"serialized-multi-layer-multi-head-attention","title":"Serialized Multi-Layer Multi-Head Attention for Neural Speaker Embedding","date":"2021-07-14","arxiv_id":"2107.06493","n_code_links":0,"syntology":null},{"paper":"/paper/cmt-convolutional-neural-networks-meet-vision","slug":"cmt-convolutional-neural-networks-meet-vision","title":"CMT: Convolutional Neural Networks Meet Vision Transformers","date":"2021-07-13","arxiv_id":"2107.06263","n_code_links":14,"syntology":{"ran":4,"of":9,"n_ran_checked":3,"n_instrument":1,"unverified":5,"pointer_only":3,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 2 honoured, 1 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 5 unverified","official":{"repos":["ggjy/cmt.pytorch"],"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":"exploiting-network-structures-to-improve","title":"Exploiting Network Structures to Improve Semantic Representation for the Financial Domain","date":"2021-07-13","arxiv_id":"2107.05885","n_code_links":0,"syntology":null},{"paper":"/paper/hat-hierarchical-aggregation-transformers-for","slug":"hat-hierarchical-aggregation-transformers-for","title":"HAT: Hierarchical Aggregation Transformers for Person Re-identification","date":"2021-07-13","arxiv_id":"2107.05946","n_code_links":1,"syntology":null},{"paper":null,"slug":"rating-facts-under-coarse-to-fine-regimes","title":"Rating Facts under Coarse-to-fine Regimes","date":"2021-07-13","arxiv_id":"2107.06051","n_code_links":0,"syntology":null},{"paper":"/paper/the-piano-inpainting-application","slug":"the-piano-inpainting-application","title":"The Piano Inpainting Application","date":"2021-07-13","arxiv_id":"2107.05944","n_code_links":2,"syntology":null},{"paper":"/paper/timbre-classification-of-musical-instruments","slug":"timbre-classification-of-musical-instruments","title":"Timbre Classification of Musical Instruments with a Deep Learning Multi-Head Attention-Based Model","date":"2021-07-13","arxiv_id":"2107.06231","n_code_links":1,"syntology":null},{"paper":null,"slug":"tscan-dialog-structure-discovery-using-scan","title":"TSCAN : Dialog Structure discovery using SCAN","date":"2021-07-13","arxiv_id":"2107.06426","n_code_links":0,"syntology":null},{"paper":"/paper/using-bert-encoding-to-tackle-the-mad-lib","slug":"using-bert-encoding-to-tackle-the-mad-lib","title":"Using BERT Encoding to Tackle the Mad-lib Attack in SMS Spam Detection","date":"2021-07-13","arxiv_id":"2107.06400","n_code_links":1,"syntology":null},{"paper":"/paper/what-do-writing-features-tell-us-about-ai","slug":"what-do-writing-features-tell-us-about-ai","title":"What do writing features tell us about AI papers?","date":"2021-07-13","arxiv_id":"2107.06310","n_code_links":1,"syntology":null},{"paper":"/paper/a-flexible-multi-task-model-for-bert-serving","slug":"a-flexible-multi-task-model-for-bert-serving","title":"A Flexible Multi-Task Model for BERT Serving","date":"2021-07-12","arxiv_id":"2107.05377","n_code_links":1,"syntology":null},{"paper":null,"slug":"asking-clarifying-questions-based-on-negative","title":"Asking Clarifying Questions Based on Negative Feedback in Conversational Search","date":"2021-07-12","arxiv_id":"2107.05760","n_code_links":0,"syntology":null},{"paper":"/paper/coberl-contrastive-bert-for-reinforcement","slug":"coberl-contrastive-bert-for-reinforcement","title":"CoBERL: Contrastive BERT for Reinforcement Learning","date":"2021-07-12","arxiv_id":"2107.05431","n_code_links":2,"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":["deepmind/dm_control"],"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":"/paper/coper-a-query-adaptable-semantics-based","slug":"coper-a-query-adaptable-semantics-based","title":"COPER: a Query-adaptable Semantics-based Search Engine for Persian COVID-19 Articles","date":"2021-07-12","arxiv_id":"2107.05722","n_code_links":1,"syntology":null},{"paper":"/paper/mect-multi-metadata-embedding-based-cross","slug":"mect-multi-metadata-embedding-based-cross","title":"MECT: Multi-Metadata Embedding based Cross-Transformer for Chinese Named Entity Recognition","date":"2021-07-12","arxiv_id":"2107.05418","n_code_links":1,"syntology":null},{"paper":"/paper/midibert-piano-large-scale-pre-training-for","slug":"midibert-piano-large-scale-pre-training-for","title":"BERT-like Pre-training for Symbolic Piano Music Classification Tasks","date":"2021-07-12","arxiv_id":"2107.05223","n_code_links":1,"syntology":null},{"paper":"/paper/moocrep-a-unified-pre-trained-embedding-of","slug":"moocrep-a-unified-pre-trained-embedding-of","title":"MOOCRep: A Unified Pre-trained Embedding of MOOC Entities","date":"2021-07-12","arxiv_id":"2107.05154","n_code_links":1,"syntology":null},{"paper":"/paper/quantifying-explainability-in-nlp-and","slug":"quantifying-explainability-in-nlp-and","title":"Quantifying Explainability in NLP and Analyzing Algorithms for Performance-Explainability Tradeoff","date":"2021-07-12","arxiv_id":"2107.05693","n_code_links":1,"syntology":{"ran":4,"of":5,"n_ran_checked":1,"n_instrument":3,"unverified":1,"pointer_only":5,"phrase":"4 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; 3 where Syntology's instrument failed) · 1 unverified","official":{"repos":["mnaylor5/quantifying-explainability"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":"/paper/split-embed-and-merge-an-accurate-table","slug":"split-embed-and-merge-an-accurate-table","title":"Split, embed and merge: An accurate table structure recognizer","date":"2021-07-12","arxiv_id":"2107.05214","n_code_links":0,"syntology":null},{"paper":"/paper/transattunet-multi-level-attention-guided-u","slug":"transattunet-multi-level-attention-guided-u","title":"TransAttUnet: Multi-level Attention-guided U-Net with Transformer for Medical Image Segmentation","date":"2021-07-12","arxiv_id":"2107.05274","n_code_links":1,"syntology":null},{"paper":"/paper/uncertainty-based-query-strategies-for-active","slug":"uncertainty-based-query-strategies-for-active","title":"Revisiting Uncertainty-based Query Strategies for Active Learning with Transformers","date":"2021-07-12","arxiv_id":"2107.05687","n_code_links":2,"syntology":{"ran":1,"of":2,"n_ran_checked":0,"n_instrument":1,"unverified":1,"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) · 1 unverified","official":{"repos":["webis-de/acl22-revisiting-uncertainty-based-query-strategies-for-active-learning-with-transformers"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"visual-transformer-with-statistical-test-for","title":"Visual Transformer with Statistical Test for COVID-19 Classification","date":"2021-07-12","arxiv_id":"2107.05334","n_code_links":0,"syntology":null},{"paper":"/paper/transformers-with-multi-modal-features-and","slug":"transformers-with-multi-modal-features-and","title":"Transformers with multi-modal features and post-fusion context for e-commerce session-based recommendation","date":"2021-07-11","arxiv_id":"2107.05124","n_code_links":0,"syntology":null},{"paper":"/paper/consensual-collaborative-training-and","slug":"consensual-collaborative-training-and","title":"Consensual Collaborative Training And Knowledge Distillation Based Facial Expression Recognition Under Noisy Annotations","date":"2021-07-10","arxiv_id":"2107.04746","n_code_links":3,"syntology":null},{"paper":"/paper/few-shot-domain-adaptation-with-polymorphic","slug":"few-shot-domain-adaptation-with-polymorphic","title":"Few-Shot Domain Adaptation with Polymorphic Transformers","date":"2021-07-10","arxiv_id":"2107.04805","n_code_links":1,"syntology":null},{"paper":null,"slug":"local-to-global-self-attention-in-vision","title":"Local-to-Global Self-Attention in Vision Transformers","date":"2021-07-10","arxiv_id":"2107.04735","n_code_links":0,"syntology":null},{"paper":null,"slug":"noise-stability-regularization-for-improving-1","title":"Noise Stability Regularization for Improving BERT Fine-tuning","date":"2021-07-10","arxiv_id":"2107.04835","n_code_links":0,"syntology":null},{"paper":"/paper/read-attend-and-code-pushing-the-limits-of","slug":"read-attend-and-code-pushing-the-limits-of","title":"Read, Attend, and Code: Pushing the Limits of Medical Codes Prediction from Clinical Notes by Machines","date":"2021-07-10","arxiv_id":"2107.10650","n_code_links":0,"syntology":null},{"paper":null,"slug":"an-initial-investigation-of-non-native-spoken","title":"An Initial Investigation of Non-Native Spoken Question-Answering","date":"2021-07-09","arxiv_id":"2107.04691","n_code_links":0,"syntology":null},{"paper":"/paper/can-deep-neural-networks-predict-data","slug":"can-deep-neural-networks-predict-data","title":"Can Deep Neural Networks Predict Data Correlations from Column Names?","date":"2021-07-09","arxiv_id":"2107.04553","n_code_links":1,"syntology":null},{"paper":"/paper/a-review-of-bangla-natural-language","slug":"a-review-of-bangla-natural-language","title":"A Review of Bangla Natural Language Processing Tasks and the Utility of Transformer Models","date":"2021-07-08","arxiv_id":"2107.03844","n_code_links":2,"syntology":null},{"paper":"/paper/affect-expression-behaviour-analysis-in-the-1","slug":"affect-expression-behaviour-analysis-in-the-1","title":"Affect Expression Behaviour Analysis in the Wild using Consensual Collaborative Training","date":"2021-07-08","arxiv_id":"2107.05736","n_code_links":1,"syntology":null},{"paper":null,"slug":"calliope-a-polyphonic-music-transformer","title":"Calliope -- A Polyphonic Music Transformer","date":"2021-07-08","arxiv_id":"2107.05546","n_code_links":0,"syntology":null},{"paper":"/paper/collaboration-of-experts-achieving-80-top-1","slug":"collaboration-of-experts-achieving-80-top-1","title":"Collaboration of Experts: Achieving 80% Top-1 Accuracy on ImageNet with 100M FLOPs","date":"2021-07-08","arxiv_id":"2107.03815","n_code_links":0,"syntology":null},{"paper":"/paper/eeg-convtransformer-for-single-trial-eeg","slug":"eeg-convtransformer-for-single-trial-eeg","title":"EEG-ConvTransformer for Single-Trial EEG based Visual Stimuli Classification","date":"2021-07-08","arxiv_id":"2107.03983","n_code_links":1,"syntology":null},{"paper":"/paper/learning-to-delegate-for-large-scale-vehicle","slug":"learning-to-delegate-for-large-scale-vehicle","title":"Learning to Delegate for Large-scale Vehicle Routing","date":"2021-07-08","arxiv_id":"2107.04139","n_code_links":1,"syntology":{"ran":0,"of":6,"n_ran_checked":0,"n_instrument":0,"unverified":6,"pointer_only":6,"phrase":"0 ran · 6 unverified","official":{"repos":["mit-wu-lab/learning-to-delegate"],"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":"bumblebee-a-transformer-for-music","title":"BumbleBee: A Transformer for Music","date":"2021-07-07","arxiv_id":"2107.03443","n_code_links":0,"syntology":null},{"paper":"/paper/can-transformer-models-measure-coherence-in-1","slug":"can-transformer-models-measure-coherence-in-1","title":"Can Transformer Models Measure Coherence In Text? Re-Thinking the Shuffle Test","date":"2021-07-07","arxiv_id":"2107.03448","n_code_links":1,"syntology":null},{"paper":null,"slug":"efficient-transformer-for-direct-speech","title":"Efficient Transformer for Direct Speech Translation","date":"2021-07-07","arxiv_id":"2107.03069","n_code_links":0,"syntology":null},{"paper":"/paper/evaluating-large-language-models-trained-on","slug":"evaluating-large-language-models-trained-on","title":"Evaluating Large Language Models Trained on Code","date":"2021-07-07","arxiv_id":"2107.03374","n_code_links":13,"syntology":{"ran":26,"of":39,"n_ran_checked":24,"n_instrument":2,"unverified":13,"pointer_only":4,"phrase":"26 ran (of which 0 constructed an object rather than computing a result; 24 with no instrument failure: 1 honoured, 0 violated, 23 with no contract checked; 2 where Syntology's instrument failed) · 13 unverified","official":{"repos":["openai/human-eval"],"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":["found_in_text","listed","official"]}}},{"paper":"/paper/glit-neural-architecture-search-for-global","slug":"glit-neural-architecture-search-for-global","title":"GLiT: Neural Architecture Search for Global and Local Image Transformer","date":"2021-07-07","arxiv_id":"2107.02960","n_code_links":2,"syntology":{"ran":2,"of":4,"n_ran_checked":2,"n_instrument":0,"unverified":2,"pointer_only":0,"phrase":"2 ran (of which 2 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) · 2 unverified; every one of the 2 samples that ran constructed an object rather than computing a result","official":{"repos":["bychen515/glit"],"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":"identifying-hijacked-reviews","title":"Identifying Hijacked Reviews","date":"2021-07-07","arxiv_id":"2107.05385","n_code_links":0,"syntology":null},{"paper":null,"slug":"languagerefer-spatial-language-model-for-3d","title":"LanguageRefer: Spatial-Language Model for 3D Visual Grounding","date":"2021-07-07","arxiv_id":"2107.03438","n_code_links":0,"syntology":null},{"paper":"/paper/learning-vision-transformer-with-squeeze-and","slug":"learning-vision-transformer-with-squeeze-and","title":"Learning Vision Transformer with Squeeze and Excitation for Facial Expression Recognition","date":"2021-07-07","arxiv_id":"2107.03107","n_code_links":0,"syntology":null},{"paper":null,"slug":"maccif-tdnn-multi-aspect-aggregation-of","title":"MACCIF-TDNN: Multi aspect aggregation of channel and context interdependence features in TDNN-based speaker verification","date":"2021-07-07","arxiv_id":"2107.03104","n_code_links":0,"syntology":null},{"paper":null,"slug":"model-selection-for-generic-contextual","title":"Model Selection for Generic Contextual Bandits","date":"2021-07-07","arxiv_id":"2107.03455","n_code_links":0,"syntology":null},{"paper":null,"slug":"not-quite-ask-a-librarian-ai-on-the-nature","title":"Not Quite 'Ask a Librarian': AI on the Nature, Value, and Future of LIS","date":"2021-07-07","arxiv_id":"2107.05383","n_code_links":0,"syntology":null},{"paper":null,"slug":"scopeformer-n-cnn-vit-hybrid-model-for","title":"Scopeformer: n-CNN-ViT Hybrid Model for Intracranial Hemorrhage Classification","date":"2021-07-07","arxiv_id":"2107.04575","n_code_links":0,"syntology":null},{"paper":"/paper/trans4trans-efficient-transformer-for","slug":"trans4trans-efficient-transformer-for","title":"Trans4Trans: Efficient Transformer for Transparent Object Segmentation to Help Visually Impaired People Navigate in the Real World","date":"2021-07-07","arxiv_id":"2107.03172","n_code_links":1,"syntology":null},{"paper":"/paper/transformer-network-for-significant-stenosis","slug":"transformer-network-for-significant-stenosis","title":"Transformer Network for Significant Stenosis Detection in CCTA of Coronary Arteries","date":"2021-07-07","arxiv_id":"2107.03035","n_code_links":1,"syntology":null},{"paper":null,"slug":"automatic-size-and-pose-homogenization-with","title":"Automatic size and pose homogenization with spatial transformer network to improve and accelerate pediatric segmentation","date":"2021-07-06","arxiv_id":"2107.02655","n_code_links":0,"syntology":null},{"paper":null,"slug":"covid-19-pneumonia-severity-prediction-using","title":"COVID-19 Pneumonia Severity Prediction using Hybrid Convolution-Attention Neural Architectures","date":"2021-07-06","arxiv_id":"2107.02672","n_code_links":0,"syntology":null},{"paper":null,"slug":"detecting-hypo-plastic-left-heart-syndrome-in","title":"Detecting Hypo-plastic Left Heart Syndrome in Fetal Ultrasound via Disease-specific Atlas Maps","date":"2021-07-06","arxiv_id":"2107.02643","n_code_links":0,"syntology":null},{"paper":null,"slug":"dtgan-differential-private-training-for","title":"DTGAN: Differential Private Training for Tabular GANs","date":"2021-07-06","arxiv_id":"2107.02521","n_code_links":0,"syntology":null},{"paper":"/paper/feature-fusion-vision-transformer-fine","slug":"feature-fusion-vision-transformer-fine","title":"Feature Fusion Vision Transformer for Fine-Grained Visual Categorization","date":"2021-07-06","arxiv_id":"2107.02341","n_code_links":1,"syntology":null},{"paper":"/paper/point-cloud-registration-using-representative","slug":"point-cloud-registration-using-representative","title":"Point Cloud Registration using Representative Overlapping Points","date":"2021-07-06","arxiv_id":"2107.02583","n_code_links":1,"syntology":{"ran":4,"of":9,"n_ran_checked":4,"n_instrument":0,"unverified":5,"pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 5 unverified","official":{"repos":["zhulf0804/ROPNet"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":5,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"contradiction-detection-in-persian-text","title":"Contradiction Detection in Persian Text","date":"2021-07-05","arxiv_id":"2107.01987","n_code_links":0,"syntology":null},{"paper":"/paper/ernie-3-0-large-scale-knowledge-enhanced-pre","slug":"ernie-3-0-large-scale-knowledge-enhanced-pre","title":"ERNIE 3.0: Large-scale Knowledge Enhanced Pre-training for Language Understanding and Generation","date":"2021-07-05","arxiv_id":"2107.02137","n_code_links":2,"syntology":null},{"paper":null,"slug":"experiments-with-adversarial-attacks-on-text","title":"Experiments with adversarial attacks on text genres","date":"2021-07-05","arxiv_id":"2107.02246","n_code_links":0,"syntology":null},{"paper":"/paper/long-short-transformer-efficient-transformers","slug":"long-short-transformer-efficient-transformers","title":"Long-Short Transformer: Efficient Transformers for Language and Vision","date":"2021-07-05","arxiv_id":"2107.02192","n_code_links":3,"syntology":{"ran":4,"of":4,"n_ran_checked":3,"n_instrument":1,"unverified":0,"pointer_only":2,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 2 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["NVIDIA/transformer-ls"],"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","unlocated"]}}},{"paper":null,"slug":"test-time-personalization-with-a-transformer","title":"Test-Time Personalization with a Transformer for Human Pose Estimation","date":"2021-07-05","arxiv_id":"2107.02133","n_code_links":0,"syntology":null},{"paper":"/paper/vision-xformers-efficient-attention-for-image","slug":"vision-xformers-efficient-attention-for-image","title":"Vision Xformers: Efficient Attention for Image Classification","date":"2021-07-05","arxiv_id":"2107.02239","n_code_links":2,"syntology":null},{"paper":"/paper/what-helps-transformers-recognize","slug":"what-helps-transformers-recognize","title":"What Helps Transformers Recognize Conversational Structure? Importance of Context, Punctuation, and Labels in Dialog Act Recognition","date":"2021-07-05","arxiv_id":"2107.02294","n_code_links":1,"syntology":null},{"paper":null,"slug":"what-makes-for-hierarchical-vision","title":"What Makes for Hierarchical Vision Transformer?","date":"2021-07-05","arxiv_id":"2107.02174","n_code_links":0,"syntology":null},{"paper":"/paper/covid-vit-classification-of-covid-19-from-ct","slug":"covid-vit-classification-of-covid-19-from-ct","title":"COVID-VIT: Classification of COVID-19 from CT chest images based on vision transformer models","date":"2021-07-04","arxiv_id":"2107.01682","n_code_links":1,"syntology":null},{"paper":null,"slug":"end-to-end-neural-coreference-resolution-1","title":"End-to-end Neural Coreference Resolution Revisited: A Simple yet Effective Baseline","date":"2021-07-04","arxiv_id":"2107.01700","n_code_links":0,"syntology":null}],"record_sha256":"921a9fa227b6f3a832d3a4c4fb1c318584005c8a58d2581d718986db4674b3ca","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}