{"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/bpe/papers/158","list_of":"/method/bpe","method":"BPE","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":158,"pages_in_order":190,"rows_per_page":100,"rows":[15701,15800],"of":18975,"counts":{"archive_papers_tagged":18975,"with_a_code_link":8675,"where_syntology_ran_a_sample":2895,"not_listed_spam_title":0,"listed":18975,"listed_where_code_ran":2895,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":2443,"every_run_a_failure_of_syntologys_instrument":452,"listed_with_a_run_with_no_instrument_failure":2443,"listed_every_run_a_failure_of_syntologys_instrument":452,"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/bpe","prev":"/method/bpe/papers/157","next":"/method/bpe/papers/159","papers":[{"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":"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":"/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"]}}},{"paper":null,"slug":"accounting-for-dependencies-in-deep-learning","title":"Accounting for Dependencies in Deep Learning Based Multiple Instance Learning for Whole Slide Imaging","date":"2021-11-01","arxiv_id":"2111.01556","n_code_links":0,"syntology":null},{"paper":"/paper/arch-net-model-distillation-for-architecture","slug":"arch-net-model-distillation-for-architecture","title":"Arch-Net: Model Distillation for Architecture Agnostic Model Deployment","date":"2021-11-01","arxiv_id":"2111.01135","n_code_links":1,"syntology":null},{"paper":null,"slug":"cross-lingual-hate-speech-detection-using","title":"Cross-lingual Hate Speech Detection using Transformer Models","date":"2021-11-01","arxiv_id":"2111.00981","n_code_links":0,"syntology":null},{"paper":"/paper/learning-to-generate-piano-music-with-sustain","slug":"learning-to-generate-piano-music-with-sustain","title":"Learning To Generate Piano Music With Sustain Pedals","date":"2021-11-01","arxiv_id":"2111.01216","n_code_links":1,"syntology":null},{"paper":"/paper/maple-masking-words-to-generate-blackout","slug":"maple-masking-words-to-generate-blackout","title":"MAPLE – MAsking words to generate blackout Poetry using sequence-to-sequence LEarning","date":"2021-11-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"vsec-transformer-based-model-for-vietnamese","title":"VSEC: Transformer-based Model for Vietnamese Spelling Correction","date":"2021-11-01","arxiv_id":"2111.00640","n_code_links":0,"syntology":null},{"paper":"/paper/wino-x-multilingual-winograd-schemas-for","slug":"wino-x-multilingual-winograd-schemas-for","title":"Wino-X: Multilingual Winograd Schemas for Commonsense Reasoning and Coreference Resolution","date":"2021-11-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/backdoor-pre-trained-models-can-transfer-to","slug":"backdoor-pre-trained-models-can-transfer-to","title":"Backdoor Pre-trained Models Can Transfer to All","date":"2021-10-30","arxiv_id":"2111.00197","n_code_links":1,"syntology":null},{"paper":"/paper/cross-modality-fusion-transformer-for","slug":"cross-modality-fusion-transformer-for","title":"Cross-Modality Fusion Transformer for Multispectral Object Detection","date":"2021-10-30","arxiv_id":"2111.00273","n_code_links":1,"syntology":null},{"paper":"/paper/dsee-dually-sparsity-embedded-efficient-1","slug":"dsee-dually-sparsity-embedded-efficient-1","title":"DSEE: Dually Sparsity-embedded Efficient Tuning of Pre-trained Language Models","date":"2021-10-30","arxiv_id":"2111.00160","n_code_links":1,"syntology":{"ran":10,"of":13,"n_ran_checked":8,"n_instrument":2,"unverified":3,"pointer_only":10,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 2 where Syntology's instrument failed) · 3 unverified","official":{"repos":["vita-group/dsee"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":3,"ran_from_kinds":["official","unlocated"]}}},{"paper":"/paper/patchformer-a-versatile-3d-transformer-based","slug":"patchformer-a-versatile-3d-transformer-based","title":"PatchFormer: An Efficient Point Transformer with Patch Attention","date":"2021-10-30","arxiv_id":"2111.00207","n_code_links":0,"syntology":null},{"paper":"/paper/amendable-generation-for-dialogue-state","slug":"amendable-generation-for-dialogue-state","title":"Amendable Generation for Dialogue State Tracking","date":"2021-10-29","arxiv_id":"2110.15659","n_code_links":1,"syntology":null},{"paper":"/paper/delayed-propagation-transformer-a-universal","slug":"delayed-propagation-transformer-a-universal","title":"Delayed Propagation Transformer: A Universal Computation Engine towards Practical Control in Cyber-Physical Systems","date":"2021-10-29","arxiv_id":"2110.15926","n_code_links":1,"syntology":{"ran":5,"of":6,"n_ran_checked":2,"n_instrument":3,"unverified":1,"pointer_only":6,"phrase":"5 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; 3 where Syntology's instrument failed) · 1 unverified","official":{"repos":["vita-group/dept"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":"/paper/rebel-relation-extraction-by-end-to-end","slug":"rebel-relation-extraction-by-end-to-end","title":"REBEL: Relation Extraction By End-to-end Language generation","date":"2021-10-29","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/structure-aware-fine-tuning-of-sequence-to","slug":"structure-aware-fine-tuning-of-sequence-to","title":"Structure-aware Fine-tuning of Sequence-to-sequence Transformers for Transition-based AMR Parsing","date":"2021-10-29","arxiv_id":"2110.15534","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-sequence-to-sequence-model-for-extracting","title":"A Sequence to Sequence Model for Extracting Multiple Product Name Entities from Dialog","date":"2021-10-28","arxiv_id":"2110.14843","n_code_links":0,"syntology":null},{"paper":"/paper/colossal-ai-a-unified-deep-learning-system","slug":"colossal-ai-a-unified-deep-learning-system","title":"Colossal-AI: A Unified Deep Learning System For Large-Scale Parallel Training","date":"2021-10-28","arxiv_id":"2110.14883","n_code_links":1,"syntology":null},{"paper":null,"slug":"dispensed-transformer-network-for","title":"Dispensed Transformer Network for Unsupervised Domain Adaptation","date":"2021-10-28","arxiv_id":"2110.14944","n_code_links":0,"syntology":null},{"paper":null,"slug":"nxmtransformer-semi-structured-sparsification","title":"NxMTransformer: Semi-Structured Sparsification for Natural Language Understanding via ADMM","date":"2021-10-28","arxiv_id":"2110.15766","n_code_links":0,"syntology":null},{"paper":null,"slug":"pruning-attention-heads-of-transformer-models","title":"Pruning Attention Heads of Transformer Models Using A* Search: A Novel Approach to Compress Big NLP Architectures","date":"2021-10-28","arxiv_id":"2110.15225","n_code_links":0,"syntology":null},{"paper":"/paper/self-supervised-representation-learning-on-1","slug":"self-supervised-representation-learning-on-1","title":"Hyper-Representations: Self-Supervised Representation Learning on Neural Network Weights for Model Characteristic Prediction","date":"2021-10-28","arxiv_id":"2110.15288","n_code_links":1,"syntology":{"ran":9,"of":15,"n_ran_checked":9,"n_instrument":0,"unverified":6,"pointer_only":15,"phrase":"9 ran (of which 6 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 1 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 6 unverified","official":{"repos":["hsg-aiml/neurips_2021-weight_space_learning"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":6,"n_ran_no_instrument_failure":9,"n_unverified":6,"ran_from_kinds":["official"]}}},{"paper":"/paper/ask-me-in-your-own-words-paraphrasing-for","slug":"ask-me-in-your-own-words-paraphrasing-for","title":"Ask me in your own words: paraphrasing for multitask question answering","date":"2021-10-27","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"detecting-dementia-from-speech-and","title":"Detecting Dementia from Speech and Transcripts using Transformers","date":"2021-10-27","arxiv_id":"2110.14769","n_code_links":0,"syntology":null},{"paper":"/paper/discovering-non-monotonic-autoregressive","slug":"discovering-non-monotonic-autoregressive","title":"Discovering Non-monotonic Autoregressive Orderings with Variational Inference","date":"2021-10-27","arxiv_id":"2110.15797","n_code_links":1,"syntology":{"ran":1,"of":7,"n_ran_checked":1,"n_instrument":0,"unverified":6,"pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 6 unverified","official":{"repos":["xuanlinli17/autoregressive_inference"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":6,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"transfer-learning-with-causal-counterfactual","title":"Transfer learning with causal counterfactual reasoning in Decision Transformers","date":"2021-10-27","arxiv_id":"2110.14355","n_code_links":0,"syntology":null},{"paper":null,"slug":"vision-transformer-for-classification-of","title":"Vision Transformer for Classification of Breast Ultrasound Images","date":"2021-10-27","arxiv_id":"2110.14731","n_code_links":0,"syntology":null},{"paper":null,"slug":"can-t-fool-me-adversarially-robust","title":"Can't Fool Me: Adversarially Robust Transformer for Video Understanding","date":"2021-10-26","arxiv_id":"2110.13950","n_code_links":0,"syntology":null},{"paper":"/paper/geometric-transformer-for-end-to-end-molecule","slug":"geometric-transformer-for-end-to-end-molecule","title":"Geometric Transformer for End-to-End Molecule Properties Prediction","date":"2021-10-26","arxiv_id":"2110.13721","n_code_links":1,"syntology":{"ran":2,"of":3,"n_ran_checked":1,"n_instrument":1,"unverified":1,"pointer_only":1,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","official":{"repos":["yoniLc/GeometricTransformerMolecule"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"leveraging-local-temporal-information-for","title":"Leveraging Local Temporal Information for Multimodal Scene Classification","date":"2021-10-26","arxiv_id":"2110.13992","n_code_links":0,"syntology":null},{"paper":"/paper/wavlm-large-scale-self-supervised-pre","slug":"wavlm-large-scale-self-supervised-pre","title":"WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing","date":"2021-10-26","arxiv_id":"2110.13900","n_code_links":9,"syntology":null},{"paper":"/paper/actions-speak-louder-than-listening","slug":"actions-speak-louder-than-listening","title":"Actions Speak Louder than Listening: Evaluating Music Style Transfer based on Editing Experience","date":"2021-10-25","arxiv_id":"2110.12855","n_code_links":1,"syntology":null},{"paper":"/paper/doctr-document-image-transformer-for","slug":"doctr-document-image-transformer-for","title":"DocTr: Document Image Transformer for Geometric Unwarping and Illumination Correction","date":"2021-10-25","arxiv_id":"2110.12942","n_code_links":2,"syntology":null},{"paper":null,"slug":"generating-artificial-texts-as-substitution","title":"Generating artificial texts as substitution or complement of training data","date":"2021-10-25","arxiv_id":"2110.13016","n_code_links":0,"syntology":null},{"paper":null,"slug":"gophormer-ego-graph-transformer-for-node","title":"Gophormer: Ego-Graph Transformer for Node Classification","date":"2021-10-25","arxiv_id":"2110.13094","n_code_links":0,"syntology":null},{"paper":"/paper/history-aware-multimodal-transformer-for","slug":"history-aware-multimodal-transformer-for","title":"History Aware Multimodal Transformer for Vision-and-Language Navigation","date":"2021-10-25","arxiv_id":"2110.13309","n_code_links":1,"syntology":{"ran":7,"of":15,"n_ran_checked":7,"n_instrument":0,"unverified":8,"pointer_only":0,"phrase":"7 ran (of which 3 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 8 unverified","official":null}},{"paper":"/paper/iconqa-a-new-benchmark-for-abstract-diagram","slug":"iconqa-a-new-benchmark-for-abstract-diagram","title":"IconQA: A New Benchmark for Abstract Diagram Understanding and Visual Language Reasoning","date":"2021-10-25","arxiv_id":"2110.13214","n_code_links":1,"syntology":{"ran":8,"of":10,"n_ran_checked":8,"n_instrument":0,"unverified":2,"pointer_only":10,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","official":{"repos":["lupantech/iconqa"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":"/paper/mvt-multi-view-vision-transformer-for-3d","slug":"mvt-multi-view-vision-transformer-for-3d","title":"MVT: Multi-view Vision Transformer for 3D Object Recognition","date":"2021-10-25","arxiv_id":"2110.13083","n_code_links":2,"syntology":{"ran":4,"of":4,"n_ran_checked":3,"n_instrument":1,"unverified":0,"pointer_only":3,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 1 honoured, 0 violated, 2 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["shanshuo/MVT"],"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":["listed","official"]}}},{"paper":null,"slug":"revisiting-cnn-for-highly-inflected-bengali","title":"Paradigm Shift in Language Modeling: Revisiting CNN for Modeling Sanskrit Originated Bengali and Hindi Language","date":"2021-10-25","arxiv_id":"2110.13032","n_code_links":0,"syntology":null},{"paper":null,"slug":"stransgan-an-empirical-study-on-transformer-1","title":"The Nuts and Bolts of Adopting Transformer in GANs","date":"2021-10-25","arxiv_id":"2110.13107","n_code_links":0,"syntology":null},{"paper":"/paper/cvt-assd-convolutional-vision-transformer","slug":"cvt-assd-convolutional-vision-transformer","title":"CvT-ASSD: Convolutional vision-Transformer Based Attentive Single Shot MultiBox Detector","date":"2021-10-24","arxiv_id":"2110.12364","n_code_links":1,"syntology":null},{"paper":"/paper/3d-anas-v2-grafting-transformer-module-on","slug":"3d-anas-v2-grafting-transformer-module-on","title":"Grafting Transformer on Automatically Designed Convolutional Neural Network for Hyperspectral Image Classification","date":"2021-10-21","arxiv_id":"2110.11084","n_code_links":1,"syntology":null},{"paper":"/paper/fast-model-editing-at-scale-1","slug":"fast-model-editing-at-scale-1","title":"Fast Model Editing at Scale","date":"2021-10-21","arxiv_id":"2110.11309","n_code_links":3,"syntology":{"ran":1,"of":1,"n_ran_checked":1,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["eric-mitchell/mend"],"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":["official"]}}},{"paper":null,"slug":"transformer-acceleration-with-dynamic-sparse","title":"Transformer Acceleration with Dynamic Sparse Attention","date":"2021-10-21","arxiv_id":"2110.11299","n_code_links":0,"syntology":null},{"paper":null,"slug":"vis-top-visual-transformer-overlay-processor","title":"Vis-TOP: Visual Transformer Overlay Processor","date":"2021-10-21","arxiv_id":"2110.10957","n_code_links":0,"syntology":null},{"paper":null,"slug":"after-unet-axial-fusion-transformer-unet-for","title":"AFTer-UNet: Axial Fusion Transformer UNet for Medical Image Segmentation","date":"2021-10-20","arxiv_id":"2110.10403","n_code_links":0,"syntology":null},{"paper":"/paper/aniformer-data-driven-3d-animation-with","slug":"aniformer-data-driven-3d-animation-with","title":"AniFormer: Data-driven 3D Animation with Transformer","date":"2021-10-20","arxiv_id":"2110.10533","n_code_links":1,"syntology":null},{"paper":null,"slug":"continual-learning-in-multilingual-nmt-via","title":"Continual Learning in Multilingual NMT via Language-Specific Embeddings","date":"2021-10-20","arxiv_id":"2110.10478","n_code_links":0,"syntology":null},{"paper":null,"slug":"esod-edge-based-task-scheduling-for-object","title":"ESOD:Edge-based Task Scheduling for Object Detection","date":"2021-10-20","arxiv_id":"2110.11342","n_code_links":0,"syntology":null},{"paper":"/paper/few-shot-temporal-action-localization-with","slug":"few-shot-temporal-action-localization-with","title":"Few-Shot Temporal Action Localization with Query Adaptive Transformer","date":"2021-10-20","arxiv_id":"2110.10552","n_code_links":1,"syntology":null},{"paper":null,"slug":"sea-graph-shell-attention-in-graph-neural","title":"SEA: Graph Shell Attention in Graph Neural Networks","date":"2021-10-20","arxiv_id":"2110.10674","n_code_links":0,"syntology":null},{"paper":null,"slug":"toward-accurate-and-reliable-iris","title":"Toward Accurate and Reliable Iris Segmentation Using Uncertainty Learning","date":"2021-10-20","arxiv_id":"2110.10334","n_code_links":0,"syntology":null},{"paper":null,"slug":"vldeformer-learning-visual-semantic","title":"VLDeformer: Vision-Language Decomposed Transformer for Fast Cross-Modal Retrieval","date":"2021-10-20","arxiv_id":"2110.11338","n_code_links":0,"syntology":null},{"paper":"/paper/a-picture-is-worth-a-thousand-words-a-unified","slug":"a-picture-is-worth-a-thousand-words-a-unified","title":"A Picture is Worth a Thousand Words: A Unified System for Diverse Captions and Rich Images Generation","date":"2021-10-19","arxiv_id":"2110.09756","n_code_links":1,"syntology":null},{"paper":null,"slug":"accelerating-framework-of-transformer-by","title":"Accelerating Framework of Transformer by Hardware Design and Model Compression Co-Optimization","date":"2021-10-19","arxiv_id":"2110.10030","n_code_links":0,"syntology":null},{"paper":null,"slug":"bilateral-vit-for-robust-fovea-localization","title":"Bilateral-ViT for Robust Fovea Localization","date":"2021-10-19","arxiv_id":"2110.09860","n_code_links":0,"syntology":null},{"paper":null,"slug":"detectornet-transformer-enhanced-spatial","title":"DetectorNet: Transformer-enhanced Spatial Temporal Graph Neural Network for Traffic Prediction","date":"2021-10-19","arxiv_id":"2111.00869","n_code_links":0,"syntology":null},{"paper":"/paper/generating-symbolic-reasoning-problems-with-1","slug":"generating-symbolic-reasoning-problems-with-1","title":"Generating Symbolic Reasoning Problems with Transformer GANs","date":"2021-10-19","arxiv_id":"2110.10054","n_code_links":1,"syntology":{"ran":6,"of":11,"n_ran_checked":6,"n_instrument":0,"unverified":5,"pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 1 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 5 unverified","official":{"repos":["reactive-systems/TGAN-SR"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":5,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"inductive-biases-and-variable-creation-in-1","title":"Inductive Biases and Variable Creation in Self-Attention Mechanisms","date":"2021-10-19","arxiv_id":"2110.10090","n_code_links":0,"syntology":null},{"paper":"/paper/permutation-invariant-graph-to-sequence-model-1","slug":"permutation-invariant-graph-to-sequence-model-1","title":"Permutation invariant graph-to-sequence model for template-free retrosynthesis and reaction prediction","date":"2021-10-19","arxiv_id":"2110.09681","n_code_links":1,"syntology":{"ran":5,"of":6,"n_ran_checked":5,"n_instrument":0,"unverified":1,"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) · 1 unverified","official":{"repos":["coleygroup/graph2smiles"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"risks-of-ai-foundation-models-in-education","title":"Risks of AI Foundation Models in Education","date":"2021-10-19","arxiv_id":"2110.10024","n_code_links":0,"syntology":null},{"paper":null,"slug":"spatial-temporal-transformer-for-3d-point","title":"Spatial-Temporal Transformer for 3D Point Cloud Sequences","date":"2021-10-19","arxiv_id":"2110.09783","n_code_links":0,"syntology":null},{"paper":"/paper/ssast-self-supervised-audio-spectrogram","slug":"ssast-self-supervised-audio-spectrogram","title":"SSAST: Self-Supervised Audio Spectrogram Transformer","date":"2021-10-19","arxiv_id":"2110.09784","n_code_links":3,"syntology":{"ran":13,"of":16,"n_ran_checked":13,"n_instrument":0,"unverified":3,"pointer_only":1,"phrase":"13 ran (of which 0 constructed an object rather than computing a result; 13 with no instrument failure: 1 honoured, 0 violated, 12 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","official":{"repos":["YuanGongND/ssast"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"paper":"/paper/unifying-multimodal-transformer-for-bi","slug":"unifying-multimodal-transformer-for-bi","title":"Unifying Multimodal Transformer for Bi-directional Image and Text Generation","date":"2021-10-19","arxiv_id":"2110.09753","n_code_links":1,"syntology":{"ran":6,"of":8,"n_ran_checked":6,"n_instrument":0,"unverified":2,"pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","official":{"repos":["researchmm/generate-it"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":"/paper/compositional-attention-disentangling-search-1","slug":"compositional-attention-disentangling-search-1","title":"Compositional Attention: Disentangling Search and Retrieval","date":"2021-10-18","arxiv_id":"2110.09419","n_code_links":3,"syntology":null},{"paper":"/paper/hrformer-high-resolution-transformer-for","slug":"hrformer-high-resolution-transformer-for","title":"HRFormer: High-Resolution Transformer for Dense Prediction","date":"2021-10-18","arxiv_id":"2110.09408","n_code_links":1,"syntology":{"ran":7,"of":11,"n_ran_checked":7,"n_instrument":0,"unverified":4,"pointer_only":0,"phrase":"7 ran (of which 7 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified; every one of the 7 samples that ran constructed an object rather than computing a result","official":{"repos":["HRNet/HRFormer"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":7,"n_ran_no_instrument_failure":7,"n_unverified":4,"ran_from_kinds":["official"]}}},{"paper":"/paper/sentimentarcs-a-novel-method-for-self","slug":"sentimentarcs-a-novel-method-for-self","title":"SentimentArcs: A Novel Method for Self-Supervised Sentiment Analysis of Time Series Shows SOTA Transformers Can Struggle Finding Narrative Arcs","date":"2021-10-18","arxiv_id":"2110.09454","n_code_links":1,"syntology":{"ran":2,"of":6,"n_ran_checked":2,"n_instrument":0,"unverified":4,"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) · 4 unverified","official":{"repos":["jon-chun/sentimentarcs_notebooks"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":4,"ran_from_kinds":["official"]}}},{"paper":"/paper/sequential-modeling-with-multiple-attributes","slug":"sequential-modeling-with-multiple-attributes","title":"Sequential Modeling with Multiple Attributes for Watchlist Recommendation in E-Commerce","date":"2021-10-18","arxiv_id":"2110.11072","n_code_links":1,"syntology":null},{"paper":"/paper/3d-retr-end-to-end-single-and-multi-view-3d","slug":"3d-retr-end-to-end-single-and-multi-view-3d","title":"3D-RETR: End-to-End Single and Multi-View 3D Reconstruction with Transformers","date":"2021-10-17","arxiv_id":"2110.08861","n_code_links":1,"syntology":null},{"paper":null,"slug":"cae-transformer-transformer-based-model-to","title":"CAE-Transformer: Transformer-based Model to Predict Invasiveness of Lung Adenocarcinoma Subsolid Nodules from Non-thin Section 3D CT Scans","date":"2021-10-17","arxiv_id":"2110.08721","n_code_links":0,"syntology":null},{"paper":"/paper/illiterate-dall-cdot-e-learns-to-compose-1","slug":"illiterate-dall-cdot-e-learns-to-compose-1","title":"Illiterate DALL-E Learns to Compose","date":"2021-10-17","arxiv_id":"2110.11405","n_code_links":1,"syntology":{"ran":3,"of":3,"n_ran_checked":3,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["singhgautam/slate"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"reminding-the-incremental-language-model-via","title":"Reminding the Incremental Language Model via Data-Free Self-Distillation","date":"2021-10-17","arxiv_id":"2110.08745","n_code_links":0,"syntology":null},{"paper":"/paper/siamese-transformer-pyramid-networks-for-real","slug":"siamese-transformer-pyramid-networks-for-real","title":"Siamese Transformer Pyramid Networks for Real-Time UAV Tracking","date":"2021-10-17","arxiv_id":"2110.08822","n_code_links":1,"syntology":null},{"paper":"/paper/a-good-prompt-is-worth-millions-of-parameters","slug":"a-good-prompt-is-worth-millions-of-parameters","title":"A Good Prompt Is Worth Millions of Parameters: Low-resource Prompt-based Learning for Vision-Language Models","date":"2021-10-16","arxiv_id":"2110.08484","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-short-study-on-compressing-decoder-based","title":"A Short Study on Compressing Decoder-Based Language Models","date":"2021-10-16","arxiv_id":"2110.08460","n_code_links":0,"syntology":null},{"paper":null,"slug":"alleviating-the-inequality-of-attention-heads-1","title":"Alleviating the Inequality of Attention Heads for Neural Machine Translation","date":"2021-10-16","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/asformer-transformer-for-action-segmentation","slug":"asformer-transformer-for-action-segmentation","title":"ASFormer: Transformer for Action Segmentation","date":"2021-10-16","arxiv_id":"2110.08568","n_code_links":1,"syntology":{"ran":1,"of":1,"n_ran_checked":1,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["chinayi/asformer"],"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":["official"]}}},{"paper":null,"slug":"attention-temperature-matters-in-abstractive-1","title":"Attention Temperature Matters in Abstractive Summarization Distillation","date":"2021-10-16","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"covid-19-detection-in-chest-x-ray-images-1","title":"COVID-19 Detection in Chest X-ray Images Using Swin-Transformer and Transformer in Transformer","date":"2021-10-16","arxiv_id":"2110.08427","n_code_links":0,"syntology":null},{"paper":null,"slug":"emotion-flip-reasoning-in-multiparty","title":"Emotion Flip Reasoning in Multiparty Conversations","date":"2021-10-16","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"emotion-style-transfer-with-a-specified","title":"Emotion Style Transfer with a Specified Intensity Using Deep Reinforcement Learning","date":"2021-10-16","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/enct5-fine-tuning-t5-encoder-for-non","slug":"enct5-fine-tuning-t5-encoder-for-non","title":"EncT5: A Framework for Fine-tuning T5 as Non-autoregressive Models","date":"2021-10-16","arxiv_id":"2110.08426","n_code_links":1,"syntology":null},{"paper":null,"slug":"evaluation-of-transfer-learning-for-polish","title":"Evaluation of Transfer Learning for Polish with a text-to-text model","date":"2021-10-16","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"hierarchical-transformer-networks-for-long","title":"Hierarchical Transformer Networks for Long-sequence and Multiple Clinical Documents Classification","date":"2021-10-16","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/hydra-a-system-for-large-multi-model-deep","slug":"hydra-a-system-for-large-multi-model-deep","title":"Hydra: A System for Large Multi-Model Deep Learning","date":"2021-10-16","arxiv_id":"2110.08633","n_code_links":1,"syntology":null},{"paper":"/paper/improving-compositional-generalization-with","slug":"improving-compositional-generalization-with","title":"Improving Compositional Generalization with Self-Training for Data-to-Text Generation","date":"2021-10-16","arxiv_id":"2110.08467","n_code_links":1,"syntology":null},{"paper":null,"slug":"knowledge-inheritance-for-pre-trained-1","title":"Knowledge Inheritance for Pre-trained Language Models","date":"2021-10-16","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"learning-rich-representation-of-keyphrases","title":"Learning Rich Representation of Keyphrases from Text","date":"2021-10-16","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"learning-to-acquire-knowledge-from-a-search","title":"Learning to Acquire Knowledge from a Search Engine for Dialogue Response Generation","date":"2021-10-16","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"multi-task-end-to-end-training-improves","title":"Multi-Task End-to-End Training Improves Conversational Recommendation","date":"2021-10-16","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"ode-transformer-an-ordinary-differential-1","title":"ODE Transformer: An Ordinary Differential Equation-Inspired Model for Sequence Generation","date":"2021-10-16","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"pagnol-an-extra-large-french-generative-model","title":"PAGnol: An Extra-Large French Generative Model","date":"2021-10-16","arxiv_id":"2110.08554","n_code_links":0,"syntology":null},{"paper":"/paper/prix-lm-pretraining-for-multilingual","slug":"prix-lm-pretraining-for-multilingual","title":"Prix-LM: Pretraining for Multilingual Knowledge Base Construction","date":"2021-10-16","arxiv_id":"2110.08443","n_code_links":1,"syntology":null},{"paper":null,"slug":"semantic-tokenizer-for-enhanced-natural","title":"Semantic Tokenizer for Enhanced Natural Language Processing","date":"2021-10-16","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"sharpness-aware-minimization-improves","title":"Sharpness-Aware Minimization Improves Language Model Generalization","date":"2021-10-16","arxiv_id":"2110.08529","n_code_links":0,"syntology":null},{"paper":null,"slug":"should-we-trust-this-summary-bayesian","title":"Should We Trust This Summary? Bayesian Abstractive Summarization to The Rescue","date":"2021-10-16","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"the-power-of-prompt-tuning-for-low-resource","title":"The Power of Prompt Tuning for Low-Resource Semantic Parsing","date":"2021-10-16","arxiv_id":"2110.08525","n_code_links":0,"syntology":null}],"record_sha256":"2cf9a0e578e818467aa29294e6d22d802a58d944dec77082f90fe45597b84cdc","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}