{"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/adam/papers/157","list_of":"/method/adam","method":"Adam","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":157,"pages_in_order":244,"rows_per_page":100,"rows":[15601,15700],"of":24390,"counts":{"archive_papers_tagged":24390,"with_a_code_link":10944,"where_syntology_ran_a_sample":3424,"not_listed_spam_title":0,"listed":24390,"listed_where_code_ran":3424,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":2899,"every_run_a_failure_of_syntologys_instrument":525,"listed_with_a_run_with_no_instrument_failure":2899,"listed_every_run_a_failure_of_syntologys_instrument":525,"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/adam","prev":"/method/adam/papers/156","next":"/method/adam/papers/158","papers":[{"paper":"/paper/self-supervised-hypergraph-transformer-for","slug":"self-supervised-hypergraph-transformer-for","title":"Self-Supervised Hypergraph Transformer for Recommender Systems","date":"2022-07-28","arxiv_id":"2207.14338","n_code_links":1,"syntology":null},{"paper":"/paper/semantic-aligned-matching-for-enhanced-detr","slug":"semantic-aligned-matching-for-enhanced-detr","title":"Semantic-Aligned Matching for Enhanced DETR Convergence and Multi-Scale Feature Fusion","date":"2022-07-28","arxiv_id":"2207.14172","n_code_links":1,"syntology":null},{"paper":"/paper/sequence-to-sequence-pretraining-for-a-less","slug":"sequence-to-sequence-pretraining-for-a-less","title":"Sequence to sequence pretraining for a less-resourced Slovenian language","date":"2022-07-28","arxiv_id":"2207.13988","n_code_links":1,"syntology":null},{"paper":null,"slug":"subtype-former-a-deep-learning-approach-for","title":"Subtype-Former: a deep learning approach for cancer subtype discovery with multi-omics data","date":"2022-07-28","arxiv_id":"2207.14639","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-variational-autoencoder-for-transformers","title":"A Variational AutoEncoder for Transformers with Nonparametric Variational Information Bottleneck","date":"2022-07-27","arxiv_id":"2207.13529","n_code_links":0,"syntology":null},{"paper":null,"slug":"are-neighbors-enough-multi-head-neural-n-gram","title":"Are Neighbors Enough? Multi-Head Neural n-gram can be Alternative to Self-attention","date":"2022-07-27","arxiv_id":"2207.13354","n_code_links":0,"syntology":null},{"paper":"/paper/avatarposer-articulated-full-body-pose","slug":"avatarposer-articulated-full-body-pose","title":"AvatarPoser: Articulated Full-Body Pose Tracking from Sparse Motion Sensing","date":"2022-07-27","arxiv_id":"2207.13784","n_code_links":1,"syntology":{"ran":6,"of":10,"n_ran_checked":5,"n_instrument":1,"unverified":4,"pointer_only":3,"phrase":"6 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; 1 where Syntology's instrument failed) · 4 unverified","official":{"repos":["eth-siplab/avatarposer"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":4,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"distributional-actor-critic-ensemble-for","title":"Distributional Actor-Critic Ensemble for Uncertainty-Aware Continuous Control","date":"2022-07-27","arxiv_id":"2207.13730","n_code_links":0,"syntology":null},{"paper":"/paper/is-attention-all-nerf-needs","slug":"is-attention-all-nerf-needs","title":"Is Attention All That NeRF Needs?","date":"2022-07-27","arxiv_id":"2207.13298","n_code_links":1,"syntology":{"ran":5,"of":7,"n_ran_checked":5,"n_instrument":0,"unverified":2,"pointer_only":0,"phrase":"5 ran (of which 5 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) · 2 unverified; every one of the 5 samples that ran constructed an object rather than computing a result","official":null}},{"paper":"/paper/soundchoice-grapheme-to-phoneme-models-with","slug":"soundchoice-grapheme-to-phoneme-models-with","title":"SoundChoice: Grapheme-to-Phoneme Models with Semantic Disambiguation","date":"2022-07-27","arxiv_id":"2207.13703","n_code_links":1,"syntology":null},{"paper":"/paper/transnorm-transformer-provides-a-strong","slug":"transnorm-transformer-provides-a-strong","title":"TransNorm: Transformer Provides a Strong Spatial Normalization Mechanism for a Deep Segmentation Model","date":"2022-07-27","arxiv_id":"2207.13415","n_code_links":1,"syntology":null},{"paper":null,"slug":"bodily-behaviors-in-social-interaction-novel","title":"Bodily Behaviors in Social Interaction: Novel Annotations and State-of-the-Art Evaluation","date":"2022-07-26","arxiv_id":"2207.12817","n_code_links":0,"syntology":null},{"paper":"/paper/bundle-mcr-towards-conversational-bundle","slug":"bundle-mcr-towards-conversational-bundle","title":"Bundle MCR: Towards Conversational Bundle Recommendation","date":"2022-07-26","arxiv_id":"2207.12628","n_code_links":1,"syntology":null},{"paper":"/paper/cross-modal-causal-relational-reasoning-for","slug":"cross-modal-causal-relational-reasoning-for","title":"Cross-Modal Causal Relational Reasoning for Event-Level Visual Question Answering","date":"2022-07-26","arxiv_id":"2207.12647","n_code_links":2,"syntology":{"ran":2,"of":2,"n_ran_checked":1,"n_instrument":1,"unverified":0,"pointer_only":2,"phrase":"2 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; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["hcplab-sysu/cmcir","yangliu9208/cmcir"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/detrs-with-hybrid-matching","slug":"detrs-with-hybrid-matching","title":"DETRs with Hybrid Matching","date":"2022-07-26","arxiv_id":"2207.13080","n_code_links":8,"syntology":{"ran":16,"of":18,"n_ran_checked":9,"n_instrument":7,"unverified":2,"pointer_only":4,"phrase":"16 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 2 honoured, 2 violated, 5 with no contract checked; 7 where Syntology's instrument failed) · 2 unverified","official":{"repos":["HDETR/H-Deformable-DETR","HDETR/H-Deformable-DETR-mmdet","HDETR/H-Detic-LVIS","HDETR/H-PETR-3D","HDETR/H-PETR-Pose"],"state":"official (archive's flag): 15 ran","n_ran":15,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":2,"ran_from_kinds":["listed","official"]}}},{"paper":null,"slug":"graph-neural-network-and-spatiotemporal","title":"Graph Neural Network and Spatiotemporal Transformer Attention for 3D Video Object Detection from Point Clouds","date":"2022-07-26","arxiv_id":"2207.12659","n_code_links":0,"syntology":null},{"paper":"/paper/group-detr-fast-training-convergence-with","slug":"group-detr-fast-training-convergence-with","title":"Group DETR: Fast DETR Training with Group-Wise One-to-Many Assignment","date":"2022-07-26","arxiv_id":"2207.13085","n_code_links":2,"syntology":null},{"paper":"/paper/multi-attention-network-for-compressed-video","slug":"multi-attention-network-for-compressed-video","title":"Multi-Attention Network for Compressed Video Referring Object Segmentation","date":"2022-07-26","arxiv_id":"2207.12622","n_code_links":1,"syntology":null},{"paper":null,"slug":"remote-medication-status-prediction-for","title":"Remote Medication Status Prediction for Individuals with Parkinson's Disease using Time-series Data from Smartphones","date":"2022-07-26","arxiv_id":"2207.13700","n_code_links":0,"syntology":null},{"paper":"/paper/training-effective-neural-sentence-encoders","slug":"training-effective-neural-sentence-encoders","title":"Training Effective Neural Sentence Encoders from Automatically Mined Paraphrases","date":"2022-07-26","arxiv_id":"2207.12759","n_code_links":1,"syntology":null},{"paper":"/paper/3d-siamese-transformer-network-for-single","slug":"3d-siamese-transformer-network-for-single","title":"3D Siamese Transformer Network for Single Object Tracking on Point Clouds","date":"2022-07-25","arxiv_id":"2207.11995","n_code_links":1,"syntology":null},{"paper":"/paper/behind-every-domain-there-is-a-shift-adapting","slug":"behind-every-domain-there-is-a-shift-adapting","title":"Behind Every Domain There is a Shift: Adapting Distortion-aware Vision Transformers for Panoramic Semantic Segmentation","date":"2022-07-25","arxiv_id":"2207.11860","n_code_links":1,"syntology":null},{"paper":null,"slug":"fine-tuning-bert-for-automatic-adme-semantic","title":"Fine-Tuning BERT for Automatic ADME Semantic Labeling in FDA Drug Labeling to Enhance Product-Specific Guidance Assessment","date":"2022-07-25","arxiv_id":"2207.12376","n_code_links":0,"syntology":null},{"paper":"/paper/igformer-interaction-graph-transformer-for","slug":"igformer-interaction-graph-transformer-for","title":"IGFormer: Interaction Graph Transformer for Skeleton-based Human Interaction Recognition","date":"2022-07-25","arxiv_id":"2207.12100","n_code_links":0,"syntology":null},{"paper":null,"slug":"is-gpt-3-all-you-need-for-visual-question","title":"Is GPT-3 all you need for Visual Question Answering in Cultural Heritage?","date":"2022-07-25","arxiv_id":"2207.12101","n_code_links":0,"syntology":null},{"paper":"/paper/jigsaw-vit-learning-jigsaw-puzzles-in-vision","slug":"jigsaw-vit-learning-jigsaw-puzzles-in-vision","title":"Jigsaw-ViT: Learning Jigsaw Puzzles in Vision Transformer","date":"2022-07-25","arxiv_id":"2207.11971","n_code_links":1,"syntology":null},{"paper":"/paper/reference-based-image-super-resolution-with","slug":"reference-based-image-super-resolution-with","title":"Reference-based Image Super-Resolution with Deformable Attention Transformer","date":"2022-07-25","arxiv_id":"2207.11938","n_code_links":1,"syntology":null},{"paper":"/paper/textrm-d-3-textrm-former-debiased-dual","slug":"textrm-d-3-textrm-former-debiased-dual","title":"D3Former: Debiased Dual Distilled Transformer for Incremental Learning","date":"2022-07-25","arxiv_id":"2208.00777","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-cognitive-study-on-semantic-similarity","title":"A Cognitive Study on Semantic Similarity Analysis of Large Corpora: A Transformer-based Approach","date":"2022-07-24","arxiv_id":"2207.11716","n_code_links":0,"syntology":null},{"paper":"/paper/affective-behaviour-analysis-using-pretrained","slug":"affective-behaviour-analysis-using-pretrained","title":"Affective Behaviour Analysis Using Pretrained Model with Facial Priori","date":"2022-07-24","arxiv_id":"2207.11679","n_code_links":1,"syntology":null},{"paper":"/paper/improving-mandarin-speech-recogntion-with","slug":"improving-mandarin-speech-recogntion-with","title":"Improving Mandarin Speech Recogntion with Block-augmented Transformer","date":"2022-07-24","arxiv_id":"2207.11697","n_code_links":2,"syntology":null},{"paper":null,"slug":"online-continual-learning-with-contrastive","title":"Online Continual Learning with Contrastive Vision Transformer","date":"2022-07-24","arxiv_id":"2207.13516","n_code_links":0,"syntology":null},{"paper":null,"slug":"savchoi-detecting-suspicious-activities-using","title":"SAVCHOI: Detecting Suspicious Activities using Dense Video Captioning with Human Object Interactions","date":"2022-07-24","arxiv_id":"2207.11838","n_code_links":0,"syntology":null},{"paper":null,"slug":"better-reasoning-behind-classification","title":"Better Reasoning Behind Classification Predictions with BERT for Fake News Detection","date":"2022-07-23","arxiv_id":"2207.11562","n_code_links":0,"syntology":null},{"paper":"/paper/combining-hybrid-architecture-and-pseudo","slug":"combining-hybrid-architecture-and-pseudo","title":"Combining Self-Training and Hybrid Architecture for Semi-supervised Abdominal Organ Segmentation","date":"2022-07-23","arxiv_id":"2207.11512","n_code_links":2,"syntology":null},{"paper":"/paper/high-resolution-swin-transformer-for","slug":"high-resolution-swin-transformer-for","title":"High-Resolution Swin Transformer for Automatic Medical Image Segmentation","date":"2022-07-23","arxiv_id":"2207.11553","n_code_links":1,"syntology":null},{"paper":null,"slug":"the-prediction-of-the-quality-of-results-in","title":"The prediction of the quality of results in Logic Synthesis using Transformer and Graph Neural Networks","date":"2022-07-23","arxiv_id":"2207.11437","n_code_links":0,"syntology":null},{"paper":null,"slug":"applying-spatiotemporal-attention-to-identify","title":"Applying Spatiotemporal Attention to Identify Distracted and Drowsy Driving with Vision Transformers","date":"2022-07-22","arxiv_id":"2207.12148","n_code_links":0,"syntology":null},{"paper":"/paper/cost-aggregation-with-4d-convolutional-swin","slug":"cost-aggregation-with-4d-convolutional-swin","title":"Cost Aggregation with 4D Convolutional Swin Transformer for Few-Shot Segmentation","date":"2022-07-22","arxiv_id":"2207.10866","n_code_links":1,"syntology":{"ran":12,"of":20,"n_ran_checked":8,"n_instrument":4,"unverified":8,"pointer_only":2,"phrase":"12 ran (of which 7 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 4 where Syntology's instrument failed) · 8 unverified","official":{"repos":["Seokju-Cho/Volumetric-Aggregation-Transformer"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":7,"n_ran_no_instrument_failure":8,"n_unverified":8,"ran_from_kinds":["official","unlocated"]}}},{"paper":"/paper/facial-expression-recognition-using-vanilla","slug":"facial-expression-recognition-using-vanilla","title":"Emotion Separation and Recognition from a Facial Expression by Generating the Poker Face with Vision Transformers","date":"2022-07-22","arxiv_id":"2207.11081","n_code_links":0,"syntology":null},{"paper":null,"slug":"learning-generalized-non-rigid-multimodal","title":"Learning Generalized Non-Rigid Multimodal Biomedical Image Registration from Generic Point Set Data","date":"2022-07-22","arxiv_id":"2207.10994","n_code_links":0,"syntology":null},{"paper":"/paper/panoptic-scene-graph-generation","slug":"panoptic-scene-graph-generation","title":"Panoptic Scene Graph Generation","date":"2022-07-22","arxiv_id":"2207.11247","n_code_links":1,"syntology":null},{"paper":"/paper/transformer-with-implicit-edges-for-particle","slug":"transformer-with-implicit-edges-for-particle","title":"Transformer with Implicit Edges for Particle-based Physics Simulation","date":"2022-07-22","arxiv_id":"2207.10860","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":["ftbabi/tie_eccv2022"],"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":"video-swin-transformers-for-egocentric-video","title":"Video Swin Transformers for Egocentric Video Understanding @ Ego4D Challenges 2022","date":"2022-07-22","arxiv_id":"2207.11329","n_code_links":0,"syntology":null},{"paper":"/paper/zero-shot-video-captioning-with-evolving","slug":"zero-shot-video-captioning-with-evolving","title":"Zero-Shot Video Captioning with Evolving Pseudo-Tokens","date":"2022-07-22","arxiv_id":"2207.11100","n_code_links":1,"syntology":null},{"paper":null,"slug":"addressing-optimism-bias-in-sequence-modeling","title":"Addressing Optimism Bias in Sequence Modeling for Reinforcement Learning","date":"2022-07-21","arxiv_id":"2207.10295","n_code_links":0,"syntology":null},{"paper":null,"slug":"an-efficient-spatio-temporal-pyramid","title":"An Efficient Spatio-Temporal Pyramid Transformer for Action Detection","date":"2022-07-21","arxiv_id":"2207.10448","n_code_links":0,"syntology":null},{"paper":null,"slug":"bigissue-a-realistic-bug-localization","title":"BigIssue: A Realistic Bug Localization Benchmark","date":"2022-07-21","arxiv_id":"2207.10739","n_code_links":0,"syntology":null},{"paper":"/paper/efficient-model-compression-with-random","slug":"efficient-model-compression-with-random","title":"Efficient model compression with Random Operation Access Specific Tile (ROAST) hashing","date":"2022-07-21","arxiv_id":"2207.10702","n_code_links":1,"syntology":null},{"paper":"/paper/focused-decoding-enables-3d-anatomical","slug":"focused-decoding-enables-3d-anatomical","title":"Focused Decoding Enables 3D Anatomical Detection by Transformers","date":"2022-07-21","arxiv_id":"2207.10774","n_code_links":1,"syntology":null},{"paper":"/paper/magic-elf-image-deraining-meets-association","slug":"magic-elf-image-deraining-meets-association","title":"Magic ELF: Image Deraining Meets Association Learning and Transformer","date":"2022-07-21","arxiv_id":"2207.10455","n_code_links":1,"syntology":null},{"paper":"/paper/multi-resolution-analysis-mra-for-approximate","slug":"multi-resolution-analysis-mra-for-approximate","title":"Multi Resolution Analysis (MRA) for Approximate Self-Attention","date":"2022-07-21","arxiv_id":"2207.10284","n_code_links":2,"syntology":null},{"paper":null,"slug":"scaling-laws-vs-model-architectures-how-does-1","title":"Scaling Laws vs Model Architectures: How does Inductive Bias Influence Scaling?","date":"2022-07-21","arxiv_id":"2207.10551","n_code_links":0,"syntology":null},{"paper":"/paper/seedformer-patch-seeds-based-point-cloud","slug":"seedformer-patch-seeds-based-point-cloud","title":"SeedFormer: Patch Seeds based Point Cloud Completion with Upsample Transformer","date":"2022-07-21","arxiv_id":"2207.10315","n_code_links":1,"syntology":{"ran":3,"of":8,"n_ran_checked":1,"n_instrument":2,"unverified":5,"pointer_only":8,"phrase":"3 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; 2 where Syntology's instrument failed) · 5 unverified","official":{"repos":["hrzhou2/seedformer"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":5,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"sequence-models-for-drone-vs-bird","title":"Sequence Models for Drone vs Bird Classification","date":"2022-07-21","arxiv_id":"2207.10409","n_code_links":0,"syntology":null},{"paper":"/paper/the-birth-of-bias-a-case-study-on-the-1","slug":"the-birth-of-bias-a-case-study-on-the-1","title":"The Birth of Bias: A case study on the evolution of gender bias in an English language model","date":"2022-07-21","arxiv_id":"2207.10245","n_code_links":1,"syntology":null},{"paper":null,"slug":"towards-efficient-adversarial-training-on","title":"Towards Efficient Adversarial Training on Vision Transformers","date":"2022-07-21","arxiv_id":"2207.10498","n_code_links":0,"syntology":null},{"paper":"/paper/weakly-supervised-object-localization-via","slug":"weakly-supervised-object-localization-via","title":"Weakly Supervised Object Localization via Transformer with Implicit Spatial Calibration","date":"2022-07-21","arxiv_id":"2207.10447","n_code_links":2,"syntology":{"ran":7,"of":10,"n_ran_checked":4,"n_instrument":3,"unverified":3,"pointer_only":0,"phrase":"7 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; 3 where Syntology's instrument failed) · 3 unverified","official":{"repos":["164140757/scm"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":3,"n_ran_no_instrument_failure":4,"n_unverified":3,"ran_from_kinds":["official"]}}},{"paper":"/paper/aiatrack-attention-in-attention-for","slug":"aiatrack-attention-in-attention-for","title":"AiATrack: Attention in Attention for Transformer Visual Tracking","date":"2022-07-20","arxiv_id":"2207.09603","n_code_links":1,"syntology":{"ran":1,"of":3,"n_ran_checked":1,"n_instrument":0,"unverified":2,"pointer_only":0,"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) · 2 unverified; the one sample that ran constructed an object rather than computing a result","official":{"repos":["Little-Podi/AiATrack"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":"/paper/bigcolor-colorization-using-a-generative","slug":"bigcolor-colorization-using-a-generative","title":"BigColor: Colorization using a Generative Color Prior for Natural Images","date":"2022-07-20","arxiv_id":"2207.09685","n_code_links":1,"syntology":{"ran":3,"of":4,"n_ran_checked":3,"n_instrument":0,"unverified":1,"pointer_only":0,"phrase":"3 ran (of which 2 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":["KIMGEONUNG/BigColor"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":2,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"htnet-anchor-free-temporal-action","title":"HTNet: Anchor-free Temporal Action Localization with Hierarchical Transformers","date":"2022-07-20","arxiv_id":"2207.09662","n_code_links":0,"syntology":null},{"paper":"/paper/locality-guidance-for-improving-vision","slug":"locality-guidance-for-improving-vision","title":"Locality Guidance for Improving Vision Transformers on Tiny Datasets","date":"2022-07-20","arxiv_id":"2207.10026","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":["lkhl/tiny-transformers"],"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":"meshmae-masked-autoencoders-for-3d-mesh-data","title":"MeshMAE: Masked Autoencoders for 3D Mesh Data Analysis","date":"2022-07-20","arxiv_id":"2207.10228","n_code_links":0,"syntology":null},{"paper":null,"slug":"unsupervised-industrial-anomaly-detection-via","title":"Unsupervised Industrial Anomaly Detection via Pattern Generative and Contrastive Networks","date":"2022-07-20","arxiv_id":"2207.09792","n_code_links":0,"syntology":null},{"paper":"/paper/vigat-bottom-up-event-recognition-and","slug":"vigat-bottom-up-event-recognition-and","title":"ViGAT: Bottom-up event recognition and explanation in video using factorized graph attention network","date":"2022-07-20","arxiv_id":"2207.09927","n_code_links":1,"syntology":null},{"paper":"/paper/abstract-demonstrations-and-adaptive","slug":"abstract-demonstrations-and-adaptive","title":"Abstract Demonstrations and Adaptive Exploration for Efficient and Stable Multi-step Sparse Reward Reinforcement Learning","date":"2022-07-19","arxiv_id":"2207.09243","n_code_links":1,"syntology":null},{"paper":"/paper/enhancing-collaborative-filtering-recommender","slug":"enhancing-collaborative-filtering-recommender","title":"Enhancing Collaborative Filtering Recommender with Prompt-Based Sentiment Analysis","date":"2022-07-19","arxiv_id":"2207.12883","n_code_links":1,"syntology":null},{"paper":null,"slug":"gafx-a-general-audio-feature-extractor","title":"GAFX: A General Audio Feature eXtractor","date":"2022-07-19","arxiv_id":"2207.09145","n_code_links":0,"syntology":null},{"paper":"/paper/investigation-of-deep-learning-models-on","slug":"investigation-of-deep-learning-models-on","title":"Investigation of deep learning models on identification of minimum signal length for precise classification of conveyor rubber belt loads","date":"2022-07-19","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/moment-centralization-based-gradient-descent","slug":"moment-centralization-based-gradient-descent","title":"Moment Centralization based Gradient Descent Optimizers for Convolutional Neural Networks","date":"2022-07-19","arxiv_id":"2207.09066","n_code_links":1,"syntology":null},{"paper":"/paper/pic-a-phrase-in-context-dataset-for-phrase","slug":"pic-a-phrase-in-context-dataset-for-phrase","title":"PiC: A Phrase-in-Context Dataset for Phrase Understanding and Semantic Search","date":"2022-07-19","arxiv_id":"2207.09068","n_code_links":1,"syntology":null},{"paper":"/paper/pre-trained-language-models-with-domain","slug":"pre-trained-language-models-with-domain","title":"Pre-trained language models with domain knowledge for biomedical extractive summarization","date":"2022-07-19","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"revealing-secrets-from-pre-trained-models","title":"Revealing Secrets From Pre-trained Models","date":"2022-07-19","arxiv_id":"2207.09539","n_code_links":0,"syntology":null},{"paper":"/paper/target-driven-structured-transformer-planner","slug":"target-driven-structured-transformer-planner","title":"Target-Driven Structured Transformer Planner for Vision-Language Navigation","date":"2022-07-19","arxiv_id":"2207.11201","n_code_links":1,"syntology":null},{"paper":null,"slug":"ttvfi-learning-trajectory-aware-transformer","title":"TTVFI: Learning Trajectory-Aware Transformer for Video Frame Interpolation","date":"2022-07-19","arxiv_id":"2207.09048","n_code_links":0,"syntology":null},{"paper":"/paper/visual-representation-learning-with","slug":"visual-representation-learning-with","title":"Vision Transformers: From Semantic Segmentation to Dense Prediction","date":"2022-07-19","arxiv_id":"2207.09339","n_code_links":3,"syntology":null},{"paper":null,"slug":"alexu-aic-at-arabic-hate-speech-2022-contrast","title":"AlexU-AIC at Arabic Hate Speech 2022: Contrast to Classify","date":"2022-07-18","arxiv_id":"2207.08557","n_code_links":0,"syntology":null},{"paper":null,"slug":"conditional-detr-v2-efficient-detection","title":"Conditional DETR V2: Efficient Detection Transformer with Box Queries","date":"2022-07-18","arxiv_id":"2207.08914","n_code_links":0,"syntology":null},{"paper":"/paper/dense-cross-query-and-support-attention","slug":"dense-cross-query-and-support-attention","title":"Dense Cross-Query-and-Support Attention Weighted Mask Aggregation for Few-Shot Segmentation","date":"2022-07-18","arxiv_id":"2207.08549","n_code_links":1,"syntology":{"ran":4,"of":5,"n_ran_checked":2,"n_instrument":2,"unverified":1,"pointer_only":5,"phrase":"4 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; 2 where Syntology's instrument failed) · 1 unverified","official":{"repos":["pawn-sxy/dcama"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":"/paper/focal-wnet-an-architecture-unifying","slug":"focal-wnet-an-architecture-unifying","title":"Focal-WNet: An Architecture Unifying Convolution and Attention for Depth Estimation","date":"2022-07-18","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/hiformer-hierarchical-multi-scale","slug":"hiformer-hierarchical-multi-scale","title":"HiFormer: Hierarchical Multi-scale Representations Using Transformers for Medical Image Segmentation","date":"2022-07-18","arxiv_id":"2207.08518","n_code_links":1,"syntology":null},{"paper":"/paper/multi-manifold-attention-for-vision","slug":"multi-manifold-attention-for-vision","title":"Multi-manifold Attention for Vision Transformers","date":"2022-07-18","arxiv_id":"2207.08569","n_code_links":0,"syntology":null},{"paper":"/paper/selection-bias-induced-spurious-correlations","slug":"selection-bias-induced-spurious-correlations","title":"Selection Bias Induced Spurious Correlations in Large Language Models","date":"2022-07-18","arxiv_id":"2207.08982","n_code_links":1,"syntology":null},{"paper":null,"slug":"word-play-for-playing-othello-reverses","title":"Word Play for Playing Othello (Reverses)","date":"2022-07-18","arxiv_id":"2207.08766","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-multibias-mitigated-and-sentiment-knowledge","title":"A Multibias-mitigated and Sentiment Knowledge Enriched Transformer for Debiasing in Multimodal Conversational Emotion Recognition","date":"2022-07-17","arxiv_id":"2207.08104","n_code_links":0,"syntology":null},{"paper":"/paper/aspect-specific-context-modeling-for-aspect","slug":"aspect-specific-context-modeling-for-aspect","title":"Aspect-specific Context Modeling for Aspect-based Sentiment Analysis","date":"2022-07-17","arxiv_id":"2207.08099","n_code_links":1,"syntology":null},{"paper":"/paper/can-large-language-models-reason-about","slug":"can-large-language-models-reason-about","title":"Can large language models reason about medical questions?","date":"2022-07-17","arxiv_id":"2207.08143","n_code_links":1,"syntology":{"ran":10,"of":10,"n_ran_checked":10,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 0 honoured, 0 violated, 10 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["vlievin/medical-reasoning"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"defect-transformer-an-efficient-hybrid","title":"Defect Transformer: An Efficient Hybrid Transformer Architecture for Surface Defect Detection","date":"2022-07-17","arxiv_id":"2207.08319","n_code_links":0,"syntology":null},{"paper":"/paper/electra-is-a-zero-shot-learner-too","slug":"electra-is-a-zero-shot-learner-too","title":"ELECTRA is a Zero-Shot Learner, Too","date":"2022-07-17","arxiv_id":"2207.08141","n_code_links":1,"syntology":{"ran":2,"of":2,"n_ran_checked":2,"n_instrument":0,"unverified":0,"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) · 0 unverified","official":{"repos":["nishiwen1214/rtd-electra"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/fashionvil-fashion-focused-vision-and","slug":"fashionvil-fashion-focused-vision-and","title":"FashionViL: Fashion-Focused Vision-and-Language Representation Learning","date":"2022-07-17","arxiv_id":"2207.08150","n_code_links":1,"syntology":null},{"paper":null,"slug":"mdm-visual-explanations-for-neural-networks","title":"MDM: Multiple Dynamic Masks for Visual Explanation of Neural Networks","date":"2022-07-17","arxiv_id":"2207.08046","n_code_links":0,"syntology":null},{"paper":null,"slug":"representation-learning-of-image-schema","title":"Representation Learning of Image Schema","date":"2022-07-17","arxiv_id":"2207.08256","n_code_links":0,"syntology":null},{"paper":null,"slug":"robust-action-governor-for-uncertain","title":"Robust Action Governor for Uncertain Piecewise Affine Systems with Non-convex Constraints and Safe Reinforcement Learning","date":"2022-07-17","arxiv_id":"2207.08240","n_code_links":0,"syntology":null},{"paper":null,"slug":"troll-tweet-detection-using-contextualized","title":"A Context-Sensitive Word Embedding Approach for The Detection of Troll Tweets","date":"2022-07-17","arxiv_id":"2207.08230","n_code_links":0,"syntology":null},{"paper":"/paper/charformer-a-glyph-fusion-based-attentive","slug":"charformer-a-glyph-fusion-based-attentive","title":"CharFormer: A Glyph Fusion based Attentive Framework for High-precision Character Image Denoising","date":"2022-07-16","arxiv_id":"2207.07798","n_code_links":1,"syntology":null},{"paper":null,"slug":"explainable-vision-transformer-enabled","title":"Explainable vision transformer enabled convolutional neural network for plant disease identification: PlantXViT","date":"2022-07-16","arxiv_id":"2207.07919","n_code_links":0,"syntology":null},{"paper":"/paper/generalizable-memory-driven-transformer-for","slug":"generalizable-memory-driven-transformer-for","title":"Mitigating Data Redundancy to Revitalize Transformer-based Long-Term Time Series Forecasting System","date":"2022-07-16","arxiv_id":"2207.07827","n_code_links":2,"syntology":null},{"paper":"/paper/generative-adversarial-networks-based-on-1","slug":"generative-adversarial-networks-based-on-1","title":"Generative Adversarial Networks Based on Transformer Encoder and Convolution Block for Hyperspectral Image Classification","date":"2022-07-16","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/jperceiver-joint-perception-network-for-depth","slug":"jperceiver-joint-perception-network-for-depth","title":"JPerceiver: Joint Perception Network for Depth, Pose and Layout Estimation in Driving Scenes","date":"2022-07-16","arxiv_id":"2207.07895","n_code_links":1,"syntology":{"ran":1,"of":2,"n_ran_checked":0,"n_instrument":1,"unverified":1,"pointer_only":2,"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":["sunnyhelen/jperceiver"],"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":"multimodal-dialog-systems-with-dual-knowledge","title":"Multimodal Dialog Systems with Dual Knowledge-enhanced Generative Pretrained Language Model","date":"2022-07-16","arxiv_id":"2207.07934","n_code_links":0,"syntology":null}],"record_sha256":"2179837e0f6914f1c6f8ca710563094abfdc5ae721c722a3c94b37334d0486ad","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}