{"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/transformer/papers/103","list_of":"/method/transformer","method":"Transformer","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":103,"pages_in_order":140,"rows_per_page":100,"rows":[10201,10300],"of":13999,"counts":{"archive_papers_tagged":13999,"with_a_code_link":6572,"where_syntology_ran_a_sample":2248,"not_listed_spam_title":0,"listed":13999,"listed_where_code_ran":2248,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":1919,"every_run_a_failure_of_syntologys_instrument":329,"listed_with_a_run_with_no_instrument_failure":1919,"listed_every_run_a_failure_of_syntologys_instrument":329,"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/transformer","prev":"/method/transformer/papers/102","next":"/method/transformer/papers/104","papers":[{"paper":"/paper/icos-protein-expression-segmentation-can","slug":"icos-protein-expression-segmentation-can","title":"ICOS Protein Expression Segmentation: Can Transformer Networks Give Better Results?","date":"2022-06-23","arxiv_id":"2206.11520","n_code_links":1,"syntology":null},{"paper":"/paper/learning-viewpoint-agnostic-visual","slug":"learning-viewpoint-agnostic-visual","title":"Learning Viewpoint-Agnostic Visual Representations by Recovering Tokens in 3D Space","date":"2022-06-23","arxiv_id":"2206.11895","n_code_links":1,"syntology":{"ran":5,"of":9,"n_ran_checked":3,"n_instrument":2,"unverified":4,"pointer_only":0,"phrase":"5 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; 2 where Syntology's instrument failed) · 4 unverified","official":{"repos":["elicassion/3dtrl"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":4,"ran_from_kinds":["official"]}}},{"paper":"/paper/revisiting-orthogonality-regularization-a","slug":"revisiting-orthogonality-regularization-a","title":"Revisiting Orthogonality Regularization: A Study for Convolutional Neural Networks in Image Classification","date":"2022-06-23","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/set-norm-and-equivariant-skip-connections-1","slug":"set-norm-and-equivariant-skip-connections-1","title":"Set Norm and Equivariant Skip Connections: Putting the Deep in Deep Sets","date":"2022-06-23","arxiv_id":"2206.11925","n_code_links":1,"syntology":{"ran":1,"of":2,"n_ran_checked":1,"n_instrument":0,"unverified":1,"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) · 1 unverified","official":{"repos":["rajesh-lab/deep_permutation_invariant"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":"/paper/toward-clinically-assisted-colorectal-polyp","slug":"toward-clinically-assisted-colorectal-polyp","title":"Toward Clinically Assisted Colorectal Polyp Recognition via Structured Cross-modal Representation Consistency","date":"2022-06-23","arxiv_id":"2206.11826","n_code_links":1,"syntology":null},{"paper":"/paper/behavior-transformers-cloning-k-modes-with","slug":"behavior-transformers-cloning-k-modes-with","title":"Behavior Transformers: Cloning $k$ modes with one stone","date":"2022-06-22","arxiv_id":"2206.11251","n_code_links":2,"syntology":{"ran":11,"of":11,"n_ran_checked":11,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"11 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; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["notmahi/bet"],"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":["listed","official"]}}},{"paper":"/paper/feature-re-calibration-based-mil-for-whole","slug":"feature-re-calibration-based-mil-for-whole","title":"Feature Re-calibration based Multiple Instance Learning for Whole Slide Image Classification","date":"2022-06-22","arxiv_id":"2206.10878","n_code_links":1,"syntology":null},{"paper":"/paper/generative-pretraining-for-black-box","slug":"generative-pretraining-for-black-box","title":"Generative Pretraining for Black-Box Optimization","date":"2022-06-22","arxiv_id":"2206.10786","n_code_links":1,"syntology":{"ran":2,"of":4,"n_ran_checked":2,"n_instrument":0,"unverified":2,"pointer_only":3,"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":["siddarthk97/bonet"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":"/paper/s2tnet-spatio-temporal-transformer-networks","slug":"s2tnet-spatio-temporal-transformer-networks","title":"S2TNet: Spatio-Temporal Transformer Networks for Trajectory Prediction in Autonomous Driving","date":"2022-06-22","arxiv_id":"2206.10902","n_code_links":1,"syntology":null},{"paper":"/paper/scaling-autoregressive-models-for-content","slug":"scaling-autoregressive-models-for-content","title":"Scaling Autoregressive Models for Content-Rich Text-to-Image Generation","date":"2022-06-22","arxiv_id":"2206.10789","n_code_links":2,"syntology":{"ran":8,"of":9,"n_ran_checked":7,"n_instrument":1,"unverified":1,"pointer_only":3,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 2 violated, 5 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","official":null}},{"paper":"/paper/towards-unsupervised-content-disentanglement-1","slug":"towards-unsupervised-content-disentanglement-1","title":"Towards Unsupervised Content Disentanglement in Sentence Representations via Syntactic Roles","date":"2022-06-22","arxiv_id":"2206.11184","n_code_links":1,"syntology":null},{"paper":null,"slug":"contranet-a-single-end-to-end-hybrid-network","title":"ConTraNet: A single end-to-end hybrid network for EEG-based and EMG-based human machine interfaces","date":"2022-06-21","arxiv_id":"2206.10677","n_code_links":0,"syntology":null},{"paper":null,"slug":"counting-varying-density-crowds-through","title":"Counting Varying Density Crowds Through Density Guided Adaptive Selection CNN and Transformer Estimation","date":"2022-06-21","arxiv_id":"2206.10075","n_code_links":0,"syntology":null},{"paper":"/paper/edgenext-efficiently-amalgamated-cnn","slug":"edgenext-efficiently-amalgamated-cnn","title":"EdgeNeXt: Efficiently Amalgamated CNN-Transformer Architecture for Mobile Vision Applications","date":"2022-06-21","arxiv_id":"2206.10589","n_code_links":8,"syntology":{"ran":7,"of":10,"n_ran_checked":7,"n_instrument":0,"unverified":3,"pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 1 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","official":{"repos":["mmaaz60/EdgeNeXt"],"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":"one-stage-action-detection-transformer","title":"One-stage Action Detection Transformer","date":"2022-06-21","arxiv_id":"2206.10080","n_code_links":0,"syntology":null},{"paper":"/paper/rethinking-symbolic-regression-datasets-and","slug":"rethinking-symbolic-regression-datasets-and","title":"Rethinking Symbolic Regression Datasets and Benchmarks for Scientific Discovery","date":"2022-06-21","arxiv_id":"2206.10540","n_code_links":1,"syntology":{"ran":2,"of":2,"n_ran_checked":0,"n_instrument":2,"unverified":0,"pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","official":{"repos":["omron-sinicx/srsd-benchmark"],"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":null,"slug":"svg-vector-font-generation-for-chinese","title":"SVG Vector Font Generation for Chinese Characters with Transformer","date":"2022-06-21","arxiv_id":"2206.10329","n_code_links":0,"syntology":null},{"paper":null,"slug":"toward-unpaired-multi-modal-medical-image","title":"Toward Unpaired Multi-modal Medical Image Segmentation via Learning Structured Semantic Consistency","date":"2022-06-21","arxiv_id":"2206.10571","n_code_links":0,"syntology":null},{"paper":"/paper/transformer-based-multi-modal-proposal-and-re","slug":"transformer-based-multi-modal-proposal-and-re","title":"Transformer-Based Multi-modal Proposal and Re-Rank for Wikipedia Image-Caption Matching","date":"2022-06-21","arxiv_id":"2206.10436","n_code_links":2,"syntology":{"ran":5,"of":6,"n_ran_checked":4,"n_instrument":1,"unverified":1,"pointer_only":0,"phrase":"5 ran (of which 3 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","official":{"repos":["mesnico/wiki-image-caption-matching"],"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":"transformers-improve-breast-cancer-diagnosis","title":"Transformers Improve Breast Cancer Diagnosis from Unregistered Multi-View Mammograms","date":"2022-06-21","arxiv_id":"2206.10096","n_code_links":0,"syntology":null},{"paper":"/paper/vicinity-vision-transformer","slug":"vicinity-vision-transformer","title":"Vicinity Vision Transformer","date":"2022-06-21","arxiv_id":"2206.10552","n_code_links":1,"syntology":{"ran":5,"of":5,"n_ran_checked":4,"n_instrument":1,"unverified":0,"pointer_only":0,"phrase":"5 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; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["opennlplab/vicinity-vision-transformer"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/global-context-vision-transformers","slug":"global-context-vision-transformers","title":"Global Context Vision Transformers","date":"2022-06-20","arxiv_id":"2206.09959","n_code_links":8,"syntology":{"ran":21,"of":36,"n_ran_checked":17,"n_instrument":4,"unverified":15,"pointer_only":15,"phrase":"21 ran (of which 8 constructed an object rather than computing a result; 17 with no instrument failure: 0 honoured, 0 violated, 17 with no contract checked; 4 where Syntology's instrument failed) · 15 unverified","official":{"repos":["nvlabs/gcvit"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":8,"n_ran_no_instrument_failure":8,"n_unverified":3,"ran_from_kinds":["listed","official"]}}},{"paper":"/paper/m-m-mix-a-multimodal-multiview-transformer","slug":"m-m-mix-a-multimodal-multiview-transformer","title":"M&M Mix: A Multimodal Multiview Transformer Ensemble","date":"2022-06-20","arxiv_id":"2206.09852","n_code_links":0,"syntology":null},{"paper":"/paper/nuqmm-quantized-matmul-for-efficient","slug":"nuqmm-quantized-matmul-for-efficient","title":"LUT-GEMM: Quantized Matrix Multiplication based on LUTs for Efficient Inference in Large-Scale Generative Language Models","date":"2022-06-20","arxiv_id":"2206.09557","n_code_links":2,"syntology":null},{"paper":"/paper/orfd-a-dataset-and-benchmark-for-off-road","slug":"orfd-a-dataset-and-benchmark-for-off-road","title":"ORFD: A Dataset and Benchmark for Off-Road Freespace Detection","date":"2022-06-20","arxiv_id":"2206.09907","n_code_links":2,"syntology":null},{"paper":"/paper/eatformer-improving-vision-transformer","slug":"eatformer-improving-vision-transformer","title":"EATFormer: Improving Vision Transformer Inspired by Evolutionary Algorithm","date":"2022-06-19","arxiv_id":"2206.09325","n_code_links":1,"syntology":{"ran":0,"of":1,"n_ran_checked":0,"n_instrument":0,"unverified":1,"pointer_only":1,"phrase":"0 ran · 1 unverified","official":{"repos":["zhangzjn/eatformer"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"paper":"/paper/learning-multiscale-transformer-models-for","slug":"learning-multiscale-transformer-models-for","title":"Learning Multiscale Transformer Models for Sequence Generation","date":"2022-06-19","arxiv_id":"2206.09337","n_code_links":1,"syntology":null},{"paper":"/paper/resource-efficient-separation-transformer","slug":"resource-efficient-separation-transformer","title":"Resource-Efficient Separation Transformer","date":"2022-06-19","arxiv_id":"2206.09507","n_code_links":1,"syntology":null},{"paper":null,"slug":"traffic-twitter-transformer-a-nature-language","title":"Traffic-Twitter Transformer: A Nature Language Processing-joined Framework For Network-wide Traffic Forecasting","date":"2022-06-19","arxiv_id":"2206.11078","n_code_links":0,"syntology":null},{"paper":null,"slug":"transfer-learning-for-robust-low-resource","title":"Transfer Learning for Robust Low-Resource Children's Speech ASR with Transformers and Source-Filter Warping","date":"2022-06-19","arxiv_id":"2206.09396","n_code_links":0,"syntology":null},{"paper":"/paper/argumentative-text-generation-in-economic","slug":"argumentative-text-generation-in-economic","title":"Argumentative Text Generation in Economic Domain","date":"2022-06-18","arxiv_id":"2206.09251","n_code_links":1,"syntology":null},{"paper":null,"slug":"automatic-summarization-of-russian-texts","title":"Automatic Summarization of Russian Texts: Comparison of Extractive and Abstractive Methods","date":"2022-06-18","arxiv_id":"2206.09253","n_code_links":0,"syntology":null},{"paper":null,"slug":"can-language-models-capture-graph-semantics","title":"Can Language Models Capture Graph Semantics? From Graphs to Language Model and Vice-Versa","date":"2022-06-18","arxiv_id":"2206.09259","n_code_links":0,"syntology":null},{"paper":"/paper/climb-a-continual-learning-benchmark-for","slug":"climb-a-continual-learning-benchmark-for","title":"CLiMB: A Continual Learning Benchmark for Vision-and-Language Tasks","date":"2022-06-18","arxiv_id":"2206.09059","n_code_links":1,"syntology":null},{"paper":"/paper/bootstrapped-transformer-for-offline","slug":"bootstrapped-transformer-for-offline","title":"Bootstrapped Transformer for Offline Reinforcement Learning","date":"2022-06-17","arxiv_id":"2206.08569","n_code_links":0,"syntology":{"ran":3,"of":4,"n_ran_checked":3,"n_instrument":0,"unverified":1,"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) · 1 unverified","official":null}},{"paper":"/paper/cmt-deeplab-clustering-mask-transformers-for-1","slug":"cmt-deeplab-clustering-mask-transformers-for-1","title":"CMT-DeepLab: Clustering Mask Transformers for Panoptic Segmentation","date":"2022-06-17","arxiv_id":"2206.08948","n_code_links":2,"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":null}},{"paper":"/paper/ctrlformer-learning-transferable-state","slug":"ctrlformer-learning-transferable-state","title":"CtrlFormer: Learning Transferable State Representation for Visual Control via Transformer","date":"2022-06-17","arxiv_id":"2206.08883","n_code_links":1,"syntology":{"ran":0,"of":1,"n_ran_checked":0,"n_instrument":0,"unverified":1,"pointer_only":0,"phrase":"0 ran · 1 unverified","official":{"repos":["YaoMarkMu/CtrlFormer_robotic"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"paper":null,"slug":"rectify-vit-shortcut-learning-by-visual","title":"Rectify ViT Shortcut Learning by Visual Saliency","date":"2022-06-17","arxiv_id":"2206.08567","n_code_links":0,"syntology":null},{"paper":"/paper/transresu-net-transformer-based-resu-net-for","slug":"transresu-net-transformer-based-resu-net-for","title":"TransResU-Net: Transformer based ResU-Net for Real-Time Colonoscopy Polyp Segmentation","date":"2022-06-17","arxiv_id":"2206.08985","n_code_links":1,"syntology":null},{"paper":"/paper/video-sparse-transformer-with-attention","slug":"video-sparse-transformer-with-attention","title":"Video Sparse Transformer With Attention-Guided Memory for Video Object Detection","date":"2022-06-17","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/adapting-self-supervised-vision-transformers","slug":"adapting-self-supervised-vision-transformers","title":"Adapting Self-Supervised Vision Transformers by Probing Attention-Conditioned Masking Consistency","date":"2022-06-16","arxiv_id":"2206.08222","n_code_links":1,"syntology":null},{"paper":null,"slug":"ai-enlightens-wireless-communication-a","title":"AI Enlightens Wireless Communication: A Transformer Backbone for CSI Feedback","date":"2022-06-16","arxiv_id":"2206.07949","n_code_links":0,"syntology":null},{"paper":null,"slug":"goodbye-wavenet-a-language-model-for-raw","title":"A Language Model With Million Context Length For Raw Audio","date":"2022-06-16","arxiv_id":"2206.08297","n_code_links":0,"syntology":null},{"paper":"/paper/long-range-graph-benchmark","slug":"long-range-graph-benchmark","title":"Long Range Graph Benchmark","date":"2022-06-16","arxiv_id":"2206.08164","n_code_links":2,"syntology":null},{"paper":"/paper/multimodal-dialogue-state-tracking-1","slug":"multimodal-dialogue-state-tracking-1","title":"Multimodal Dialogue State Tracking","date":"2022-06-16","arxiv_id":"2206.07898","n_code_links":1,"syntology":null},{"paper":"/paper/omnimae-single-model-masked-pretraining-on","slug":"omnimae-single-model-masked-pretraining-on","title":"OmniMAE: Single Model Masked Pretraining on Images and Videos","date":"2022-06-16","arxiv_id":"2206.08356","n_code_links":1,"syntology":{"ran":1,"of":1,"n_ran_checked":0,"n_instrument":1,"unverified":0,"pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["facebookresearch/omnivore"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/zero-shot-video-question-answering-via-frozen","slug":"zero-shot-video-question-answering-via-frozen","title":"Zero-Shot Video Question Answering via Frozen Bidirectional Language Models","date":"2022-06-16","arxiv_id":"2206.08155","n_code_links":3,"syntology":{"ran":14,"of":34,"n_ran_checked":14,"n_instrument":0,"unverified":20,"pointer_only":1,"phrase":"14 ran (of which 9 constructed an object rather than computing a result; 14 with no instrument failure: 1 honoured, 1 violated, 12 with no contract checked; 0 where Syntology's instrument failed) · 20 unverified","official":{"repos":["antoyang/FrozenBiLM"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":7,"ran_from_kinds":["listed"]}}},{"paper":null,"slug":"a-projection-based-k-space-transformer","title":"A Projection-Based K-space Transformer Network for Undersampled Radial MRI Reconstruction with Limited Training Subjects","date":"2022-06-15","arxiv_id":"2206.07219","n_code_links":0,"syntology":null},{"paper":"/paper/amr-alignment-paying-attention-to-cross","slug":"amr-alignment-paying-attention-to-cross","title":"Cross-lingual AMR Aligner: Paying Attention to Cross-Attention","date":"2022-06-15","arxiv_id":"2206.07587","n_code_links":1,"syntology":null},{"paper":null,"slug":"born-for-auto-tagging-faster-and-better-with","title":"Born for Auto-Tagging: Faster and better with new objective functions","date":"2022-06-15","arxiv_id":"2206.07264","n_code_links":0,"syntology":null},{"paper":"/paper/fixeval-execution-based-evaluation-of-program","slug":"fixeval-execution-based-evaluation-of-program","title":"FixEval: Execution-based Evaluation of Program Fixes for Programming Problems","date":"2022-06-15","arxiv_id":"2206.07796","n_code_links":1,"syntology":{"ran":11,"of":14,"n_ran_checked":11,"n_instrument":0,"unverified":3,"pointer_only":2,"phrase":"11 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; 0 where Syntology's instrument failed) · 3 unverified","official":{"repos":["mahimanzum/fixeval"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":11,"n_unverified":3,"ran_from_kinds":["official"]}}},{"paper":"/paper/how-gnns-facilitate-cnns-in-mining-geometric","slug":"how-gnns-facilitate-cnns-in-mining-geometric","title":"How GNNs Facilitate CNNs in Mining Geometric Information from Large-Scale Medical Images","date":"2022-06-15","arxiv_id":"2206.07599","n_code_links":1,"syntology":null},{"paper":"/paper/xmorpher-full-transformer-for-deformable","slug":"xmorpher-full-transformer-for-deformable","title":"XMorpher: Full Transformer for Deformable Medical Image Registration via Cross Attention","date":"2022-06-15","arxiv_id":"2206.07349","n_code_links":1,"syntology":null},{"paper":"/paper/efficient-decoder-free-object-detection-with","slug":"efficient-decoder-free-object-detection-with","title":"Efficient Decoder-free Object Detection with Transformers","date":"2022-06-14","arxiv_id":"2206.06829","n_code_links":2,"syntology":{"ran":1,"of":1,"n_ran_checked":1,"n_instrument":0,"unverified":0,"pointer_only":1,"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":["Pealing/DFFT"],"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":"it-s-time-for-artistic-correspondence-in-1","title":"It's Time for Artistic Correspondence in Music and Video","date":"2022-06-14","arxiv_id":"2206.07148","n_code_links":0,"syntology":null},{"paper":null,"slug":"object-scene-representation-transformer","title":"Object Scene Representation Transformer","date":"2022-06-14","arxiv_id":"2206.06922","n_code_links":0,"syntology":null},{"paper":"/paper/stand-alone-inter-frame-attention-in-video-1","slug":"stand-alone-inter-frame-attention-in-video-1","title":"Stand-Alone Inter-Frame Attention in Video Models","date":"2022-06-14","arxiv_id":"2206.06931","n_code_links":1,"syntology":null},{"paper":"/paper/transvg-end-to-end-visual-grounding-with-1","slug":"transvg-end-to-end-visual-grounding-with-1","title":"TransVG++: End-to-End Visual Grounding with Language Conditioned Vision Transformer","date":"2022-06-14","arxiv_id":"2206.06619","n_code_links":1,"syntology":null},{"paper":"/paper/exploring-structure-aware-transformer-over-1","slug":"exploring-structure-aware-transformer-over-1","title":"Exploring Structure-aware Transformer over Interaction Proposals for Human-Object Interaction Detection","date":"2022-06-13","arxiv_id":"2206.06291","n_code_links":1,"syntology":null},{"paper":"/paper/featurized-query-r-cnn","slug":"featurized-query-r-cnn","title":"Featurized Query R-CNN","date":"2022-06-13","arxiv_id":"2206.06258","n_code_links":1,"syntology":null},{"paper":null,"slug":"multimodal-learning-with-transformers-a","title":"Multimodal Learning with Transformers: A Survey","date":"2022-06-13","arxiv_id":"2206.06488","n_code_links":0,"syntology":null},{"paper":null,"slug":"on-the-learning-of-non-autoregressive","title":"On the Learning of Non-Autoregressive Transformers","date":"2022-06-13","arxiv_id":"2206.05975","n_code_links":0,"syntology":null},{"paper":null,"slug":"recommender-transformers-with-behavior","title":"Recommender Transformers with Behavior Pathways","date":"2022-06-13","arxiv_id":"2206.06804","n_code_links":0,"syntology":null},{"paper":"/paper/transformer-lesion-tracker","slug":"transformer-lesion-tracker","title":"Transformer Lesion Tracker","date":"2022-06-13","arxiv_id":"2206.06252","n_code_links":1,"syntology":null},{"paper":null,"slug":"seatrans-learning-segmentation-assisted","title":"SeATrans: Learning Segmentation-Assisted diagnosis model via Transformer","date":"2022-06-12","arxiv_id":"2206.05763","n_code_links":0,"syntology":null},{"paper":null,"slug":"kaggle-kinship-recognition-challenge","title":"Kaggle Kinship Recognition Challenge: Introduction of Convolution-Free Model to boost conventional","date":"2022-06-11","arxiv_id":"2206.05488","n_code_links":0,"syntology":null},{"paper":"/paper/multi-instrument-music-synthesis-with","slug":"multi-instrument-music-synthesis-with","title":"Multi-instrument Music Synthesis with Spectrogram Diffusion","date":"2022-06-11","arxiv_id":"2206.05408","n_code_links":1,"syntology":{"ran":4,"of":4,"n_ran_checked":4,"n_instrument":0,"unverified":0,"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) · 0 unverified","official":{"repos":["magenta/music-spectrogram-diffusion"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"generalizable-neural-radiance-fields-for","title":"Generalizable Neural Radiance Fields for Novel View Synthesis with Transformer","date":"2022-06-10","arxiv_id":"2206.05375","n_code_links":0,"syntology":null},{"paper":"/paper/nagphormer-neighborhood-aggregation-graph","slug":"nagphormer-neighborhood-aggregation-graph","title":"NAGphormer: A Tokenized Graph Transformer for Node Classification in Large Graphs","date":"2022-06-10","arxiv_id":"2206.04910","n_code_links":1,"syntology":{"ran":2,"of":2,"n_ran_checked":1,"n_instrument":1,"unverified":0,"pointer_only":0,"phrase":"2 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; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["jhl-hust/nagphormer"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/structcoder-structure-aware-transformer-for","slug":"structcoder-structure-aware-transformer-for","title":"StructCoder: Structure-Aware Transformer for Code Generation","date":"2022-06-10","arxiv_id":"2206.05239","n_code_links":1,"syntology":{"ran":6,"of":8,"n_ran_checked":6,"n_instrument":0,"unverified":2,"pointer_only":1,"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":["reddy-lab-code-research/structcoder"],"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":null,"slug":"a-sparse-polynomial-chaos-expansion-based","title":"A Sparse Polynomial Chaos Expansion-Based Method for Probabilistic Transient Stability Assessment and Enhancement","date":"2022-06-09","arxiv_id":"2206.04244","n_code_links":0,"syntology":null},{"paper":null,"slug":"building-spatio-temporal-transformers-for","title":"Building Spatio-temporal Transformers for Egocentric 3D Pose Estimation","date":"2022-06-09","arxiv_id":"2206.04785","n_code_links":0,"syntology":null},{"paper":"/paper/efficient-and-robust-2d-to-bev-representation","slug":"efficient-and-robust-2d-to-bev-representation","title":"Efficient and Robust 2D-to-BEV Representation Learning via Geometry-guided Kernel Transformer","date":"2022-06-09","arxiv_id":"2206.04584","n_code_links":1,"syntology":{"ran":3,"of":4,"n_ran_checked":3,"n_instrument":0,"unverified":1,"pointer_only":1,"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) · 1 unverified","official":{"repos":["hustvl/gkt"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":"/paper/efficient-human-pose-estimation-via-3d-event","slug":"efficient-human-pose-estimation-via-3d-event","title":"Efficient Human Pose Estimation via 3D Event Point Cloud","date":"2022-06-09","arxiv_id":"2206.04511","n_code_links":1,"syntology":{"ran":7,"of":8,"n_ran_checked":3,"n_instrument":4,"unverified":1,"pointer_only":3,"phrase":"7 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; 4 where Syntology's instrument failed) · 1 unverified","official":{"repos":["masterhow/eventpointpose"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":"/paper/neural-prompt-search","slug":"neural-prompt-search","title":"Neural Prompt Search","date":"2022-06-09","arxiv_id":"2206.04673","n_code_links":1,"syntology":{"ran":8,"of":9,"n_ran_checked":5,"n_instrument":3,"unverified":1,"pointer_only":2,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 2 honoured, 0 violated, 3 with no contract checked; 3 where Syntology's instrument failed) · 1 unverified","official":{"repos":["ZhangYuanhan-AI/NOAH"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":"/paper/pointnext-revisiting-pointnet-with-improved","slug":"pointnext-revisiting-pointnet-with-improved","title":"PointNeXt: Revisiting PointNet++ with Improved Training and Scaling Strategies","date":"2022-06-09","arxiv_id":"2206.04670","n_code_links":3,"syntology":{"ran":10,"of":17,"n_ran_checked":4,"n_instrument":6,"unverified":7,"pointer_only":0,"phrase":"10 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; 6 where Syntology's instrument failed) · 7 unverified","official":{"repos":["guochengqian/pointnext"],"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":["community","listed","official"]}}},{"paper":"/paper/spatial-entropy-regularization-for-vision","slug":"spatial-entropy-regularization-for-vision","title":"Spatial Entropy as an Inductive Bias for Vision Transformers","date":"2022-06-09","arxiv_id":"2206.04636","n_code_links":1,"syntology":{"ran":4,"of":4,"n_ran_checked":0,"n_instrument":4,"unverified":0,"pointer_only":0,"phrase":"4 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; 4 where Syntology's instrument failed) · 0 unverified","official":{"repos":["helia95/sar"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"stndt-modeling-neural-population-activity","title":"STNDT: Modeling Neural Population Activity with a Spatiotemporal Transformer","date":"2022-06-09","arxiv_id":"2206.04727","n_code_links":0,"syntology":null},{"paper":"/paper/swinchex-multi-label-classification-on-chest","slug":"swinchex-multi-label-classification-on-chest","title":"SwinCheX: Multi-label classification on chest X-ray images with transformers","date":"2022-06-09","arxiv_id":"2206.04246","n_code_links":1,"syntology":null},{"paper":"/paper/unveiling-transformers-with-lego-a-synthetic","slug":"unveiling-transformers-with-lego-a-synthetic","title":"Unveiling Transformers with LEGO: a synthetic reasoning task","date":"2022-06-09","arxiv_id":"2206.04301","n_code_links":1,"syntology":{"ran":2,"of":2,"n_ran_checked":0,"n_instrument":2,"unverified":0,"pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","official":{"repos":["yizhangzzz/transformers-lego"],"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/blind-face-restoration-benchmark-datasets-and","slug":"blind-face-restoration-benchmark-datasets-and","title":"Blind Face Restoration: Benchmark Datasets and a Baseline Model","date":"2022-06-08","arxiv_id":"2206.03697","n_code_links":2,"syntology":null},{"paper":"/paper/cass-cross-architectural-self-supervision-for","slug":"cass-cross-architectural-self-supervision-for","title":"CASS: Cross Architectural Self-Supervision for Medical Image Analysis","date":"2022-06-08","arxiv_id":"2206.04170","n_code_links":1,"syntology":null},{"paper":null,"slug":"few-shot-question-generation-for-personalized","title":"Few-shot Question Generation for Personalized Feedback in Intelligent Tutoring Systems","date":"2022-06-08","arxiv_id":"2206.04187","n_code_links":0,"syntology":null},{"paper":"/paper/patch-based-object-centric-transformers-for","slug":"patch-based-object-centric-transformers-for","title":"Patch-based Object-centric Transformers for Efficient Video Generation","date":"2022-06-08","arxiv_id":"2206.04003","n_code_links":1,"syntology":null},{"paper":null,"slug":"set-interdependence-transformer-set-to","title":"Set Interdependence Transformer: Set-to-Sequence Neural Networks for Permutation Learning and Structure Prediction","date":"2022-06-08","arxiv_id":"2206.03720","n_code_links":0,"syntology":null},{"paper":null,"slug":"detr-taming-your-multi-scale-detection","title":"DETR++: Taming Your Multi-Scale Detection Transformer","date":"2022-06-07","arxiv_id":"2206.02977","n_code_links":0,"syntology":null},{"paper":null,"slug":"how-to-dissect-a-muppet-the-structure-of","title":"How to Dissect a Muppet: The Structure of Transformer Embedding Spaces","date":"2022-06-07","arxiv_id":"2206.03529","n_code_links":0,"syntology":null},{"paper":null,"slug":"parotid-gland-mri-segmentation-based-on-swin","title":"Parotid Gland MRI Segmentation Based on Swin-Unet and Multimodal Images","date":"2022-06-07","arxiv_id":"2206.03336","n_code_links":0,"syntology":null},{"paper":null,"slug":"portfolio-transformer-for-attention-based","title":"Portfolio Transformer for Attention-Based Asset Allocation","date":"2022-06-07","arxiv_id":"2206.03246","n_code_links":0,"syntology":null},{"paper":"/paper/raat-relation-augmented-attention-transformer","slug":"raat-relation-augmented-attention-transformer","title":"RAAT: Relation-Augmented Attention Transformer for Relation Modeling in Document-Level Event Extraction","date":"2022-06-07","arxiv_id":"2206.03377","n_code_links":1,"syntology":null},{"paper":null,"slug":"structured-context-transformer-for-generic","title":"Structured Context Transformer for Generic Event Boundary Detection","date":"2022-06-07","arxiv_id":"2206.02985","n_code_links":0,"syntology":null},{"paper":"/paper/transformer-based-personalized-attention","slug":"transformer-based-personalized-attention","title":"Transformer-based Personalized Attention Mechanism for Medical Images with Clinical Records","date":"2022-06-07","arxiv_id":"2206.03003","n_code_links":1,"syntology":null},{"paper":"/paper/tutel-adaptive-mixture-of-experts-at-scale","slug":"tutel-adaptive-mixture-of-experts-at-scale","title":"Tutel: Adaptive Mixture-of-Experts at Scale","date":"2022-06-07","arxiv_id":"2206.03382","n_code_links":2,"syntology":{"ran":6,"of":9,"n_ran_checked":6,"n_instrument":0,"unverified":3,"pointer_only":3,"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) · 3 unverified","official":{"repos":["microsoft/Swin-Transformer","microsoft/tutel"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":3,"ran_from_kinds":["official"]}}},{"paper":"/paper/a-computational-psycholinguistic-evaluation","slug":"a-computational-psycholinguistic-evaluation","title":"A computational psycholinguistic evaluation of the syntactic abilities of Galician BERT models at the interface of dependency resolution and training time","date":"2022-06-06","arxiv_id":"2206.02440","n_code_links":1,"syntology":null},{"paper":"/paper/mmformer-multimodal-medical-transformer-for","slug":"mmformer-multimodal-medical-transformer-for","title":"mmFormer: Multimodal Medical Transformer for Incomplete Multimodal Learning of Brain Tumor Segmentation","date":"2022-06-06","arxiv_id":"2206.02425","n_code_links":1,"syntology":{"ran":13,"of":17,"n_ran_checked":12,"n_instrument":1,"unverified":4,"pointer_only":1,"phrase":"13 ran (of which 0 constructed an object rather than computing a result; 12 with no instrument failure: 0 honoured, 0 violated, 12 with no contract checked; 1 where Syntology's instrument failed) · 4 unverified","official":{"repos":["yaozhang93/mmformer"],"state":"official (archive's flag): 13 ran","n_ran":13,"n_constructed":0,"n_ran_no_instrument_failure":12,"n_unverified":4,"ran_from_kinds":["official"]}}},{"paper":"/paper/multi-behavior-sequential-recommendation-with","slug":"multi-behavior-sequential-recommendation-with","title":"Multi-Behavior Sequential Recommendation with Temporal Graph Transformer","date":"2022-06-06","arxiv_id":"2206.02687","n_code_links":1,"syntology":null},{"paper":"/paper/scaling-vision-transformers-to-gigapixel-1","slug":"scaling-vision-transformers-to-gigapixel-1","title":"Scaling Vision Transformers to Gigapixel Images via Hierarchical Self-Supervised Learning","date":"2022-06-06","arxiv_id":"2206.02647","n_code_links":2,"syntology":{"ran":2,"of":2,"n_ran_checked":2,"n_instrument":0,"unverified":0,"pointer_only":2,"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) · 0 unverified; every one of the 2 samples that ran constructed an object rather than computing a result","official":{"repos":["mahmoodlab/hipt"],"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":"spam-detection-using-bert","title":"Spam Detection Using BERT","date":"2022-06-06","arxiv_id":"2206.02443","n_code_links":0,"syntology":null},{"paper":null,"slug":"cainnflow-convolutional-block-attention","title":"CAINNFlow: Convolutional block Attention modules and Invertible Neural Networks Flow for anomaly detection and localization tasks","date":"2022-06-04","arxiv_id":"2206.01992","n_code_links":0,"syntology":null},{"paper":null,"slug":"cross-modal-clinical-graph-transformer-for-1","title":"Cross-modal Clinical Graph Transformer for Ophthalmic Report Generation","date":"2022-06-04","arxiv_id":"2206.01988","n_code_links":0,"syntology":null}],"record_sha256":"72e65d7ef7ae422fd61473c2dfdc2a853a9ebb76f86e2e386e5ea47f0e243f6e","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}