{"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/residual-connection/papers/161","list_of":"/method/residual-connection","method":"Residual Connection","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":161,"pages_in_order":285,"rows_per_page":100,"rows":[16001,16100],"of":28401,"counts":{"archive_papers_tagged":28401,"with_a_code_link":12847,"where_syntology_ran_a_sample":3897,"not_listed_spam_title":0,"listed":28401,"listed_where_code_ran":3897,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":3291,"every_run_a_failure_of_syntologys_instrument":606,"listed_with_a_run_with_no_instrument_failure":3291,"listed_every_run_a_failure_of_syntologys_instrument":606,"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/residual-connection","prev":"/method/residual-connection/papers/160","next":"/method/residual-connection/papers/162","papers":[{"paper":null,"slug":"summer-wechat-neural-machine-translation","title":"Summer: WeChat Neural Machine Translation Systems for the WMT22 Biomedical Translation Task","date":"2022-11-28","arxiv_id":"2211.15022","n_code_links":0,"syntology":null},{"paper":"/paper/superpoint-transformer-for-3d-scene-instance","slug":"superpoint-transformer-for-3d-scene-instance","title":"Superpoint Transformer for 3D Scene Instance Segmentation","date":"2022-11-28","arxiv_id":"2211.15766","n_code_links":1,"syntology":null},{"paper":"/paper/3d-point-positional-encoding-for-multi-camera","slug":"3d-point-positional-encoding-for-multi-camera","title":"3DPPE: 3D Point Positional Encoding for Multi-Camera 3D Object Detection Transformers","date":"2022-11-27","arxiv_id":"2211.14710","n_code_links":1,"syntology":null},{"paper":"/paper/a-knowledge-based-learning-framework-for-self","slug":"a-knowledge-based-learning-framework-for-self","title":"A Knowledge-based Learning Framework for Self-supervised Pre-training Towards Enhanced Recognition of Biomedical Microscopy Images","date":"2022-11-27","arxiv_id":"2211.14715","n_code_links":1,"syntology":null},{"paper":"/paper/a-time-series-is-worth-64-words-long-term","slug":"a-time-series-is-worth-64-words-long-term","title":"A Time Series is Worth 64 Words: Long-term Forecasting with Transformers","date":"2022-11-27","arxiv_id":"2211.14730","n_code_links":8,"syntology":{"ran":17,"of":30,"n_ran_checked":16,"n_instrument":1,"unverified":13,"pointer_only":0,"phrase":"17 ran (of which 2 constructed an object rather than computing a result; 16 with no instrument failure: 0 honoured, 0 violated, 16 with no contract checked; 1 where Syntology's instrument failed) · 13 unverified","official":{"repos":["yuqinie98/patchtst"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":6,"ran_from_kinds":["listed","official"]}}},{"paper":null,"slug":"awte-bert-attending-to-wordpiece-tokenization","title":"ESIE-BERT: Enriching Sub-words Information Explicitly with BERT for Joint Intent Classification and SlotFilling","date":"2022-11-27","arxiv_id":"2211.14829","n_code_links":0,"syntology":null},{"paper":null,"slug":"detect-localize-repair-a-unified-framework","title":"Detect-Localize-Repair: A Unified Framework for Learning to Debug with CodeT5","date":"2022-11-27","arxiv_id":"2211.14875","n_code_links":0,"syntology":null},{"paper":"/paper/prototype-as-query-for-few-shot-semantic","slug":"prototype-as-query-for-few-shot-semantic","title":"Prototype as Query for Few Shot Semantic Segmentation","date":"2022-11-27","arxiv_id":"2211.14764","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":["leileicao/protoformer"],"state":"official: no sample here; runs from other or unrecorded repositories","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["unlocated"]}}},{"paper":"/paper/semantic-aware-local-global-vision","slug":"semantic-aware-local-global-vision","title":"Semantic-Aware Local-Global Vision Transformer","date":"2022-11-27","arxiv_id":"2211.14705","n_code_links":0,"syntology":null},{"paper":null,"slug":"understanding-bloom-an-empirical-study-on","title":"Understanding BLOOM: An empirical study on diverse NLP tasks","date":"2022-11-27","arxiv_id":"2211.14865","n_code_links":0,"syntology":null},{"paper":"/paper/cddfuse-correlation-driven-dual-branch","slug":"cddfuse-correlation-driven-dual-branch","title":"CDDFuse: Correlation-Driven Dual-Branch Feature Decomposition for Multi-Modality Image Fusion","date":"2022-11-26","arxiv_id":"2211.14461","n_code_links":3,"syntology":{"ran":3,"of":3,"n_ran_checked":0,"n_instrument":3,"unverified":0,"pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","official":{"repos":["zhaozixiang1228/mmif-cddfuse"],"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","unlocated"]}}},{"paper":"/paper/cross-field-transformer-for-diabetic","slug":"cross-field-transformer-for-diabetic","title":"Cross-Field Transformer for Diabetic Retinopathy Grading on Two-field Fundus Images","date":"2022-11-26","arxiv_id":"2211.14552","n_code_links":1,"syntology":null},{"paper":"/paper/how-crucial-is-transformer-in-decision","slug":"how-crucial-is-transformer-in-decision","title":"How Crucial is Transformer in Decision Transformer?","date":"2022-11-26","arxiv_id":"2211.14655","n_code_links":1,"syntology":{"ran":2,"of":5,"n_ran_checked":1,"n_instrument":1,"unverified":3,"pointer_only":2,"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) · 3 unverified","official":{"repos":["max7born/decision-lstm"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":3,"ran_from_kinds":["official","unlocated"]}}},{"paper":"/paper/patchgt-transformer-over-non-trainable","slug":"patchgt-transformer-over-non-trainable","title":"PatchGT: Transformer over Non-trainable Clusters for Learning Graph Representations","date":"2022-11-26","arxiv_id":"2211.14425","n_code_links":1,"syntology":null},{"paper":null,"slug":"sgce-font-skeleton-guided-channel-expansion","title":"SGCE-Font: Skeleton Guided Channel Expansion for Chinese Font Generation","date":"2022-11-26","arxiv_id":"2211.14475","n_code_links":0,"syntology":null},{"paper":null,"slug":"transformer-based-model-for-word-level","title":"Transformer-based Model for Word Level Language Identification in Code-mixed Kannada-English Texts","date":"2022-11-26","arxiv_id":"2211.14459","n_code_links":0,"syntology":null},{"paper":"/paper/unsupervised-wildfire-change-detection-based","slug":"unsupervised-wildfire-change-detection-based","title":"Unsupervised Wildfire Change Detection based on Contrastive Learning","date":"2022-11-26","arxiv_id":"2211.14654","n_code_links":1,"syntology":null},{"paper":"/paper/a-system-for-morphology-task-generalization","slug":"a-system-for-morphology-task-generalization","title":"A System for Morphology-Task Generalization via Unified Representation and Behavior Distillation","date":"2022-11-25","arxiv_id":"2211.14296","n_code_links":1,"syntology":null},{"paper":null,"slug":"aggregated-text-transformer-for-scene-text","title":"Aggregated Text Transformer for Scene Text Detection","date":"2022-11-25","arxiv_id":"2211.13984","n_code_links":0,"syntology":null},{"paper":"/paper/an-analysis-of-social-biases-present-in-bert","slug":"an-analysis-of-social-biases-present-in-bert","title":"An Analysis of Social Biases Present in BERT Variants Across Multiple Languages","date":"2022-11-25","arxiv_id":"2211.14402","n_code_links":1,"syntology":{"ran":3,"of":3,"n_ran_checked":0,"n_instrument":3,"unverified":0,"pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","official":{"repos":["parishadbehnam/social-biases-in-bert-variants-across-multiple-languages"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"asynchronous-event-triggered-control-for-non","title":"Asynchronous Event-Triggered Control for Non-Linear Systems","date":"2022-11-25","arxiv_id":"2211.13846","n_code_links":0,"syntology":null},{"paper":"/paper/batmannet-bi-branch-masked-graph-transformer","slug":"batmannet-bi-branch-masked-graph-transformer","title":"BatmanNet: Bi-branch Masked Graph Transformer Autoencoder for Molecular Representation","date":"2022-11-25","arxiv_id":"2211.13979","n_code_links":1,"syntology":null},{"paper":null,"slug":"degenerate-swin-to-win-plain-window-based","title":"Degenerate Swin to Win: Plain Window-based Transformer without Sophisticated Operations","date":"2022-11-25","arxiv_id":"2211.14255","n_code_links":0,"syntology":null},{"paper":"/paper/finetuning-bert-on-partially-annotated-ner","slug":"finetuning-bert-on-partially-annotated-ner","title":"Finetuning BERT on Partially Annotated NER Corpora","date":"2022-11-25","arxiv_id":"2211.14360","n_code_links":1,"syntology":null},{"paper":"/paper/galvatron-efficient-transformer-training-over","slug":"galvatron-efficient-transformer-training-over","title":"Galvatron: Efficient Transformer Training over Multiple GPUs Using Automatic Parallelism","date":"2022-11-25","arxiv_id":"2211.13878","n_code_links":3,"syntology":{"ran":3,"of":3,"n_ran_checked":1,"n_instrument":2,"unverified":0,"pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","official":{"repos":["pku-dair/hetu"],"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":["listed","official"]}}},{"paper":null,"slug":"gpt-3-driven-pedagogical-agents-for-training","title":"GPT-3-driven pedagogical agents for training children's curious question-asking skills","date":"2022-11-25","arxiv_id":"2211.14228","n_code_links":0,"syntology":null},{"paper":"/paper/interaction-visual-transformer-for-egocentric","slug":"interaction-visual-transformer-for-egocentric","title":"Interaction Region Visual Transformer for Egocentric Action Anticipation","date":"2022-11-25","arxiv_id":"2211.14154","n_code_links":1,"syntology":null},{"paper":null,"slug":"molecular-joint-representation-learning-via","title":"Molecular Joint Representation Learning via Multi-modal Information","date":"2022-11-25","arxiv_id":"2211.14042","n_code_links":0,"syntology":null},{"paper":"/paper/mpcvit-searching-for-mpc-friendly-vision","slug":"mpcvit-searching-for-mpc-friendly-vision","title":"MPCViT: Searching for Accurate and Efficient MPC-Friendly Vision Transformer with Heterogeneous Attention","date":"2022-11-25","arxiv_id":"2211.13955","n_code_links":1,"syntology":null},{"paper":null,"slug":"overcoming-catastrophic-forgetting-by-xai","title":"Overcoming Catastrophic Forgetting by XAI","date":"2022-11-25","arxiv_id":"2211.14177","n_code_links":0,"syntology":null},{"paper":"/paper/re-2tal-rewiring-pretrained-video-backbones","slug":"re-2tal-rewiring-pretrained-video-backbones","title":"Re^2TAL: Rewiring Pretrained Video Backbones for Reversible Temporal Action Localization","date":"2022-11-25","arxiv_id":"2211.14053","n_code_links":1,"syntology":null},{"paper":null,"slug":"rust-latent-neural-scene-representations-from","title":"RUST: Latent Neural Scene Representations from Unposed Imagery","date":"2022-11-25","arxiv_id":"2211.14306","n_code_links":0,"syntology":null},{"paper":"/paper/spatial-spectral-transformer-for","slug":"spatial-spectral-transformer-for","title":"Spatial-Spectral Transformer for Hyperspectral Image Denoising","date":"2022-11-25","arxiv_id":"2211.14090","n_code_links":3,"syntology":{"ran":0,"of":2,"n_ran_checked":0,"n_instrument":0,"unverified":2,"pointer_only":2,"phrase":"0 ran · 2 unverified","official":{"repos":["myuli/sst"],"state":"official: not harvested","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":[]}}},{"paper":null,"slug":"spatial-temporal-attention-network-for-open","title":"Spatial-Temporal Attention Network for Open-Set Fine-Grained Image Recognition","date":"2022-11-25","arxiv_id":"2211.13940","n_code_links":0,"syntology":null},{"paper":null,"slug":"taotf-a-two-stage-approximately-orthogonal","title":"TAOTF: A Two-stage Approximately Orthogonal Training Framework in Deep Neural Networks","date":"2022-11-25","arxiv_id":"2211.13902","n_code_links":0,"syntology":null},{"paper":null,"slug":"the-naughtyformer-a-transformer-understands","title":"The Naughtyformer: A Transformer Understands Offensive Humor","date":"2022-11-25","arxiv_id":"2211.14369","n_code_links":0,"syntology":null},{"paper":"/paper/uperformer-a-multi-scale-transformer-based","slug":"uperformer-a-multi-scale-transformer-based","title":"MUSTER: A Multi-scale Transformer-based Decoder for Semantic Segmentation","date":"2022-11-25","arxiv_id":"2211.13928","n_code_links":2,"syntology":null},{"paper":"/paper/a-self-attention-ansatz-for-ab-initio-quantum","slug":"a-self-attention-ansatz-for-ab-initio-quantum","title":"A Self-Attention Ansatz for Ab-initio Quantum Chemistry","date":"2022-11-24","arxiv_id":"2211.13672","n_code_links":3,"syntology":{"ran":7,"of":7,"n_ran_checked":7,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"7 ran (of which 0 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) · 0 unverified","official":{"repos":["deepmind/ferminet"],"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":"beyond-mahalanobis-based-scores-for-textual","title":"Beyond Mahalanobis-Based Scores for Textual OOD Detection","date":"2022-11-24","arxiv_id":"2211.13527","n_code_links":0,"syntology":null},{"paper":"/paper/cross-aggregation-transformer-for-image","slug":"cross-aggregation-transformer-for-image","title":"Cross Aggregation Transformer for Image Restoration","date":"2022-11-24","arxiv_id":"2211.13654","n_code_links":3,"syntology":{"ran":7,"of":11,"n_ran_checked":5,"n_instrument":2,"unverified":4,"pointer_only":0,"phrase":"7 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; 2 where Syntology's instrument failed) · 4 unverified","official":{"repos":["zhengchen1999/cat"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":5,"n_ran_no_instrument_failure":5,"n_unverified":4,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"dba-efficient-transformer-with-dynamic","title":"DBA: Efficient Transformer with Dynamic Bilinear Low-Rank Attention","date":"2022-11-24","arxiv_id":"2211.16368","n_code_links":0,"syntology":null},{"paper":null,"slug":"efficient-zero-shot-visual-search-via-target","title":"Efficient Zero-shot Visual Search via Target and Context-aware Transformer","date":"2022-11-24","arxiv_id":"2211.13470","n_code_links":0,"syntology":null},{"paper":null,"slug":"index-indonesian-idiom-and-expression-dataset","title":"InDEX: Indonesian Idiom and Expression Dataset for Cloze Test","date":"2022-11-24","arxiv_id":"2211.13376","n_code_links":0,"syntology":null},{"paper":"/paper/more-comprehensive-facial-inversion-for-more","slug":"more-comprehensive-facial-inversion-for-more","title":"More comprehensive facial inversion for more effective expression recognition","date":"2022-11-24","arxiv_id":"2211.13564","n_code_links":1,"syntology":null},{"paper":null,"slug":"motion-guided-global-local-aggregation","title":"Motion-Guided Global-Local Aggregation Transformer Network for Precipitation Nowcasting","date":"2022-11-24","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/nqe-n-ary-query-embedding-for-complex-query","slug":"nqe-n-ary-query-embedding-for-complex-query","title":"NQE: N-ary Query Embedding for Complex Query Answering over Hyper-Relational Knowledge Graphs","date":"2022-11-24","arxiv_id":"2211.13469","n_code_links":1,"syntology":null},{"paper":null,"slug":"using-selective-masking-as-a-bridge-between","title":"Using Selective Masking as a Bridge between Pre-training and Fine-tuning","date":"2022-11-24","arxiv_id":"2211.13815","n_code_links":0,"syntology":null},{"paper":"/paper/video-test-time-adaptation-for-action","slug":"video-test-time-adaptation-for-action","title":"Video Test-Time Adaptation for Action Recognition","date":"2022-11-24","arxiv_id":"2211.15393","n_code_links":1,"syntology":null},{"paper":null,"slug":"augop-inject-transformation-into-neural","title":"AugOp: Inject Transformation into Neural Operator","date":"2022-11-23","arxiv_id":"2211.12514","n_code_links":0,"syntology":null},{"paper":"/paper/coda-prompt-continual-decomposed-attention","slug":"coda-prompt-continual-decomposed-attention","title":"CODA-Prompt: COntinual Decomposed Attention-based Prompting for Rehearsal-Free Continual Learning","date":"2022-11-23","arxiv_id":"2211.13218","n_code_links":2,"syntology":{"ran":2,"of":3,"n_ran_checked":1,"n_instrument":1,"unverified":1,"pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","official":{"repos":["gt-ripl/coda-prompt"],"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":"completing-point-cloud-from-few-points-by","title":"Completing point cloud from few points by Wasserstein GAN and Transformers","date":"2022-11-23","arxiv_id":"2211.12746","n_code_links":0,"syntology":null},{"paper":null,"slug":"data-augmentation-vision-transformer-for-fine","title":"Data Augmentation Vision Transformer for Fine-grained Image Classification","date":"2022-11-23","arxiv_id":"2211.12879","n_code_links":0,"syntology":null},{"paper":null,"slug":"dynamic-appearance-a-video-representation-for","title":"Dynamic Appearance: A Video Representation for Action Recognition with Joint Training","date":"2022-11-23","arxiv_id":"2211.12748","n_code_links":0,"syntology":null},{"paper":"/paper/evaluating-and-mitigating-static-bias-of","slug":"evaluating-and-mitigating-static-bias-of","title":"Mitigating and Evaluating Static Bias of Action Representations in the Background and the Foreground","date":"2022-11-23","arxiv_id":"2211.12883","n_code_links":1,"syntology":null},{"paper":"/paper/ghostnetv2-enhance-cheap-operation-with-long","slug":"ghostnetv2-enhance-cheap-operation-with-long","title":"GhostNetV2: Enhance Cheap Operation with Long-Range Attention","date":"2022-11-23","arxiv_id":"2211.12905","n_code_links":12,"syntology":{"ran":8,"of":10,"n_ran_checked":8,"n_instrument":0,"unverified":2,"pointer_only":10,"phrase":"8 ran (of which 7 constructed an object rather than computing a result; 8 with no instrument failure: 1 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","official":null}},{"paper":"/paper/global-temporal-difference-network-for-action","slug":"global-temporal-difference-network-for-action","title":"Global Temporal Difference Network for Action Recognition","date":"2022-11-23","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/housediffusion-vector-floorplan-generation","slug":"housediffusion-vector-floorplan-generation","title":"HouseDiffusion: Vector Floorplan Generation via a Diffusion Model with Discrete and Continuous Denoising","date":"2022-11-23","arxiv_id":"2211.13287","n_code_links":1,"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: 1 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["aminshabani/house_diffusion"],"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":null,"slug":"identification-of-surface-defects-on-solar-pv","title":"Identification of Surface Defects on Solar PV Panels and Wind Turbine Blades using Attention based Deep Learning Model","date":"2022-11-23","arxiv_id":"2211.15374","n_code_links":0,"syntology":null},{"paper":"/paper/improving-visual-textual-sentiment-analysis","slug":"improving-visual-textual-sentiment-analysis","title":"Holistic Visual-Textual Sentiment Analysis with Prior Models","date":"2022-11-23","arxiv_id":"2211.12981","n_code_links":1,"syntology":null},{"paper":null,"slug":"sarcasm-detection-framework-using-emotion-and","title":"Sarcasm Detection Framework Using Context, Emotion and Sentiment Features","date":"2022-11-23","arxiv_id":"2211.13014","n_code_links":0,"syntology":null},{"paper":null,"slug":"seat-stable-and-explainable-attention","title":"SEAT: Stable and Explainable Attention","date":"2022-11-23","arxiv_id":"2211.13290","n_code_links":0,"syntology":null},{"paper":"/paper/ss-cxr-multitask-representation-learning","slug":"ss-cxr-multitask-representation-learning","title":"SPCXR: Self-supervised Pretraining using Chest X-rays Towards a Domain Specific Foundation Model","date":"2022-11-23","arxiv_id":"2211.12944","n_code_links":0,"syntology":null},{"paper":"/paper/svformer-semi-supervised-video-transformer","slug":"svformer-semi-supervised-video-transformer","title":"SVFormer: Semi-supervised Video Transformer for Action Recognition","date":"2022-11-23","arxiv_id":"2211.13222","n_code_links":1,"syntology":null},{"paper":"/paper/transvcl-attention-enhanced-video-copy","slug":"transvcl-attention-enhanced-video-copy","title":"TransVCL: Attention-enhanced Video Copy Localization Network with Flexible Supervision","date":"2022-11-23","arxiv_id":"2211.13090","n_code_links":2,"syntology":null},{"paper":null,"slug":"word-level-representation-from-bytes-for","title":"Word-Level Representation From Bytes For Language Modeling","date":"2022-11-23","arxiv_id":"2211.12677","n_code_links":0,"syntology":null},{"paper":"/paper/a-scope-sensitive-and-result-attentive-model","slug":"a-scope-sensitive-and-result-attentive-model","title":"A Scope Sensitive and Result Attentive Model for Multi-Intent Spoken Language Understanding","date":"2022-11-22","arxiv_id":"2211.12220","n_code_links":0,"syntology":null},{"paper":"/paper/conv2former-a-simple-transformer-style","slug":"conv2former-a-simple-transformer-style","title":"Conv2Former: A Simple Transformer-Style ConvNet for Visual Recognition","date":"2022-11-22","arxiv_id":"2211.11943","n_code_links":2,"syntology":null},{"paper":"/paper/coreference-resolution-through-a-seq2seq","slug":"coreference-resolution-through-a-seq2seq","title":"Coreference Resolution through a seq2seq Transition-Based System","date":"2022-11-22","arxiv_id":"2211.12142","n_code_links":1,"syntology":null},{"paper":"/paper/detrs-with-collaborative-hybrid-assignments","slug":"detrs-with-collaborative-hybrid-assignments","title":"DETRs with Collaborative Hybrid Assignments Training","date":"2022-11-22","arxiv_id":"2211.12860","n_code_links":6,"syntology":{"ran":4,"of":5,"n_ran_checked":4,"n_instrument":0,"unverified":1,"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) · 1 unverified","official":null}},{"paper":null,"slug":"generalizable-industrial-visual-anomaly","title":"Generalizable Industrial Visual Anomaly Detection with Self-Induction Vision Transformer","date":"2022-11-22","arxiv_id":"2211.12311","n_code_links":0,"syntology":null},{"paper":null,"slug":"hypertuning-toward-adapting-large-language","title":"HyperTuning: Toward Adapting Large Language Models without Back-propagation","date":"2022-11-22","arxiv_id":"2211.12485","n_code_links":0,"syntology":null},{"paper":null,"slug":"magicpony-learning-articulated-3d-animals-in","title":"MagicPony: Learning Articulated 3D Animals in the Wild","date":"2022-11-22","arxiv_id":"2211.12497","n_code_links":0,"syntology":null},{"paper":"/paper/mariancg-a-code-generation-transformer-model","slug":"mariancg-a-code-generation-transformer-model","title":"MarianCG: a code generation transformer model inspired by machine translation","date":"2022-11-22","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"mss-depthnet-depth-prediction-with-multi-step","title":"MSS-DepthNet: Depth Prediction with Multi-Step Spiking Neural Network","date":"2022-11-22","arxiv_id":"2211.12156","n_code_links":0,"syntology":null},{"paper":null,"slug":"olga-an-ontology-and-lstm-based-approach-for","title":"OLGA : An Ontology and LSTM-based approach for generating Arithmetic Word Problems (AWPs) of transfer type","date":"2022-11-22","arxiv_id":"2211.12164","n_code_links":0,"syntology":null},{"paper":"/paper/one-eye-is-all-you-need-lightweight-ensembles","slug":"one-eye-is-all-you-need-lightweight-ensembles","title":"One Eye is All You Need: Lightweight Ensembles for Gaze Estimation with Single Encoders","date":"2022-11-22","arxiv_id":"2211.11936","n_code_links":1,"syntology":null},{"paper":"/paper/prompttts-controllable-text-to-speech-with","slug":"prompttts-controllable-text-to-speech-with","title":"PromptTTS: Controllable Text-to-Speech with Text Descriptions","date":"2022-11-22","arxiv_id":"2211.12171","n_code_links":1,"syntology":null},{"paper":"/paper/pvt3d-point-voxel-transformers-for-place","slug":"pvt3d-point-voxel-transformers-for-place","title":"CASSPR: Cross Attention Single Scan Place Recognition","date":"2022-11-22","arxiv_id":"2211.12542","n_code_links":1,"syntology":null},{"paper":"/paper/retrieval-augmented-multimodal-language","slug":"retrieval-augmented-multimodal-language","title":"Retrieval-Augmented Multimodal Language Modeling","date":"2022-11-22","arxiv_id":"2211.12561","n_code_links":0,"syntology":null},{"paper":"/paper/teach-detr-better-training-detr-with-teachers","slug":"teach-detr-better-training-detr-with-teachers","title":"Teach-DETR: Better Training DETR with Teachers","date":"2022-11-22","arxiv_id":"2211.11953","n_code_links":1,"syntology":{"ran":10,"of":11,"n_ran_checked":7,"n_instrument":3,"unverified":1,"pointer_only":3,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 1 honoured, 1 violated, 5 with no contract checked; 3 where Syntology's instrument failed) · 1 unverified","official":{"repos":["leonhlj/teach-detr"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":"/paper/transformer-based-multi-grained-features-for","slug":"transformer-based-multi-grained-features-for","title":"Transformer Based Multi-Grained Features for Unsupervised Person Re-Identification","date":"2022-11-22","arxiv_id":"2211.12280","n_code_links":1,"syntology":null},{"paper":null,"slug":"tranvit-an-integrated-vision-transformer","title":"Computer Vision for Transit Travel Time Prediction: An End-to-End Framework Using Roadside Urban Imagery","date":"2022-11-22","arxiv_id":"2211.12322","n_code_links":0,"syntology":null},{"paper":"/paper/u-flow-a-u-shaped-normalizing-flow-for","slug":"u-flow-a-u-shaped-normalizing-flow-for","title":"U-Flow: A U-shaped Normalizing Flow for Anomaly Detection with Unsupervised Threshold","date":"2022-11-22","arxiv_id":"2211.12353","n_code_links":3,"syntology":null},{"paper":"/paper/blur-interpolation-transformer-for-real-world","slug":"blur-interpolation-transformer-for-real-world","title":"Blur Interpolation Transformer for Real-World Motion from Blur","date":"2022-11-21","arxiv_id":"2211.11423","n_code_links":1,"syntology":null},{"paper":"/paper/cbeaf-adapting-enhanced-continual-pretraining","slug":"cbeaf-adapting-enhanced-continual-pretraining","title":"AF Adapter: Continual Pretraining for Building Chinese Biomedical Language Model","date":"2022-11-21","arxiv_id":"2211.11363","n_code_links":1,"syntology":null},{"paper":null,"slug":"classification-of-human-monkeypox-disease","title":"Classification of Human Monkeypox Disease Using Deep Learning Models and Attention Mechanisms","date":"2022-11-21","arxiv_id":"2211.15459","n_code_links":0,"syntology":null},{"paper":null,"slug":"deanthropomorphising-nlp-can-a-language-model","title":"Deanthropomorphising NLP: Can a Language Model Be Conscious?","date":"2022-11-21","arxiv_id":"2211.11483","n_code_links":0,"syntology":null},{"paper":null,"slug":"enhancing-self-consistency-and-performance-of","title":"Enhancing Self-Consistency and Performance of Pre-Trained Language Models through Natural Language Inference","date":"2022-11-21","arxiv_id":"2211.11875","n_code_links":0,"syntology":null},{"paper":"/paper/exploring-the-efficacy-of-pre-trained","slug":"exploring-the-efficacy-of-pre-trained","title":"Exploring the Efficacy of Pre-trained Checkpoints in Text-to-Music Generation Task","date":"2022-11-21","arxiv_id":"2211.11216","n_code_links":2,"syntology":null},{"paper":"/paper/iitransformer-a-unified-approach-to","slug":"iitransformer-a-unified-approach-to","title":"iiTransformer: A Unified Approach to Exploiting Local and Non-Local Information for Image Restoration","date":"2022-11-21","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"l3cube-hindbert-and-devbert-pre-trained-bert","title":"L3Cube-HindBERT and DevBERT: Pre-Trained BERT Transformer models for Devanagari based Hindi and Marathi Languages","date":"2022-11-21","arxiv_id":"2211.11418","n_code_links":0,"syntology":null},{"paper":"/paper/l3cube-mahasbert-and-hindsbert-sentence-bert","slug":"l3cube-mahasbert-and-hindsbert-sentence-bert","title":"L3Cube-MahaSBERT and HindSBERT: Sentence BERT Models and Benchmarking BERT Sentence Representations for Hindi and Marathi","date":"2022-11-21","arxiv_id":"2211.11187","n_code_links":1,"syntology":null},{"paper":"/paper/language-in-a-bottle-language-model-guided","slug":"language-in-a-bottle-language-model-guided","title":"Language in a Bottle: Language Model Guided Concept Bottlenecks for Interpretable Image Classification","date":"2022-11-21","arxiv_id":"2211.11158","n_code_links":2,"syntology":null},{"paper":"/paper/mean-shift-mask-transformer-for-unseen-object","slug":"mean-shift-mask-transformer-for-unseen-object","title":"Mean Shift Mask Transformer for Unseen Object Instance Segmentation","date":"2022-11-21","arxiv_id":"2211.11679","n_code_links":1,"syntology":null},{"paper":null,"slug":"multi-spectral-image-classification-with","title":"Multi-Spectral Image Classification with Ultra-Lean Complex-Valued Models","date":"2022-11-21","arxiv_id":"2211.11797","n_code_links":0,"syntology":null},{"paper":"/paper/n-gram-in-swin-transformers-for-efficient","slug":"n-gram-in-swin-transformers-for-efficient","title":"N-Gram in Swin Transformers for Efficient Lightweight Image Super-Resolution","date":"2022-11-21","arxiv_id":"2211.11436","n_code_links":2,"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":["rami0205/ngramswin"],"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/normalizing-flow-with-variational-latent","slug":"normalizing-flow-with-variational-latent","title":"Normalizing Flow with Variational Latent Representation","date":"2022-11-21","arxiv_id":"2211.11638","n_code_links":1,"syntology":null},{"paper":"/paper/pointclip-v2-adapting-clip-for-powerful-3d","slug":"pointclip-v2-adapting-clip-for-powerful-3d","title":"PointCLIP V2: Prompting CLIP and GPT for Powerful 3D Open-world Learning","date":"2022-11-21","arxiv_id":"2211.11682","n_code_links":2,"syntology":{"ran":8,"of":12,"n_ran_checked":4,"n_instrument":4,"unverified":4,"pointer_only":4,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 1 violated, 3 with no contract checked; 4 where Syntology's instrument failed) · 4 unverified","official":{"repos":["yangyangyang127/pointclip_v2"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":4,"ran_from_kinds":["official"]}}},{"paper":"/paper/ric-cnn-rotation-invariant-coordinate","slug":"ric-cnn-rotation-invariant-coordinate","title":"RIC-CNN: Rotation-Invariant Coordinate Convolutional Neural Network","date":"2022-11-21","arxiv_id":"2211.11812","n_code_links":1,"syntology":null},{"paper":null,"slug":"tcbert-a-technical-report-for-chinese-topic","title":"TCBERT: A Technical Report for Chinese Topic Classification BERT","date":"2022-11-21","arxiv_id":"2211.11304","n_code_links":0,"syntology":null}],"record_sha256":"db861217940d67f6de2134e776af9100b7d1b3f7d2847a0680f2f6c7ec0faf3b","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}