{"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/label-smoothing/papers/71","list_of":"/method/label-smoothing","method":"Label Smoothing","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":71,"pages_in_order":144,"rows_per_page":100,"rows":[7001,7100],"of":14327,"counts":{"archive_papers_tagged":14327,"with_a_code_link":6651,"where_syntology_ran_a_sample":2259,"not_listed_spam_title":0,"listed":14327,"listed_where_code_ran":2259,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":1920,"every_run_a_failure_of_syntologys_instrument":339,"listed_with_a_run_with_no_instrument_failure":1920,"listed_every_run_a_failure_of_syntologys_instrument":339,"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/label-smoothing","prev":"/method/label-smoothing/papers/70","next":"/method/label-smoothing/papers/72","papers":[{"paper":null,"slug":"pubic-symphysis-fetal-head-segmentation-using","title":"Pubic Symphysis-Fetal Head Segmentation Using Pure Transformer with Bi-level Routing Attention","date":"2023-09-30","arxiv_id":"2310.00289","n_code_links":0,"syntology":null},{"paper":"/paper/quiz-an-arbitrary-volumetric-point-matching","slug":"quiz-an-arbitrary-volumetric-point-matching","title":"QUIZ: An Arbitrary Volumetric Point Matching Method for Medical Image Registration","date":"2023-09-30","arxiv_id":"2310.00296","n_code_links":2,"syntology":null},{"paper":null,"slug":"unlocking-bias-detection-leveraging","title":"Unlocking Bias Detection: Leveraging Transformer-Based Models for Content Analysis","date":"2023-09-30","arxiv_id":"2310.00347","n_code_links":0,"syntology":null},{"paper":null,"slug":"upar-a-kantian-inspired-prompting-framework","title":"UPAR: A Kantian-Inspired Prompting Framework for Enhancing Large Language Model Capabilities","date":"2023-09-30","arxiv_id":"2310.01441","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-large-language-model-approach-to","title":"A Large Language Model Approach to Educational Survey Feedback Analysis","date":"2023-09-29","arxiv_id":"2309.17447","n_code_links":0,"syntology":null},{"paper":null,"slug":"an-evaluation-of-gpt-models-for-phenotype","title":"An evaluation of GPT models for phenotype concept recognition","date":"2023-09-29","arxiv_id":"2309.17169","n_code_links":0,"syntology":null},{"paper":"/paper/benchmarking-the-abilities-of-large-language","slug":"benchmarking-the-abilities-of-large-language","title":"Benchmarking the Abilities of Large Language Models for RDF Knowledge Graph Creation and Comprehension: How Well Do LLMs Speak Turtle?","date":"2023-09-29","arxiv_id":"2309.17122","n_code_links":3,"syntology":null},{"paper":"/paper/craft-customizing-llms-by-creating-and","slug":"craft-customizing-llms-by-creating-and","title":"CRAFT: Customizing LLMs by Creating and Retrieving from Specialized Toolsets","date":"2023-09-29","arxiv_id":"2309.17428","n_code_links":2,"syntology":{"ran":0,"of":5,"n_ran_checked":0,"n_instrument":0,"unverified":5,"pointer_only":5,"phrase":"0 ran · 5 unverified","official":{"repos":["lifan-yuan/craft"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":5,"ran_from_kinds":[]}}},{"paper":"/paper/dyval-graph-informed-dynamic-evaluation-of","slug":"dyval-graph-informed-dynamic-evaluation-of","title":"DyVal: Dynamic Evaluation of Large Language Models for Reasoning Tasks","date":"2023-09-29","arxiv_id":"2309.17167","n_code_links":1,"syntology":null},{"paper":"/paper/enhancing-large-language-models-in-coding","slug":"enhancing-large-language-models-in-coding","title":"Enhancing Large Language Models in Coding Through Multi-Perspective Self-Consistency","date":"2023-09-29","arxiv_id":"2309.17272","n_code_links":1,"syntology":{"ran":8,"of":14,"n_ran_checked":7,"n_instrument":1,"unverified":6,"pointer_only":0,"phrase":"8 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; 1 where Syntology's instrument failed) · 6 unverified","official":{"repos":["skpig/MPSC"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":6,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"gsdc-transformer-an-efficient-and-effective","title":"GSDC Transformer: An Efficient and Effective Cue Fusion for Monocular Multi-Frame Depth Estimation","date":"2023-09-29","arxiv_id":"2309.17059","n_code_links":0,"syntology":null},{"paper":null,"slug":"ifast-weakly-supervised-interpretable-face","title":"IFAST: Weakly Supervised Interpretable Face Anti-spoofing from Single-shot Binocular NIR Images","date":"2023-09-29","arxiv_id":"2309.17399","n_code_links":0,"syntology":null},{"paper":"/paper/llm-deliberation-evaluating-llms-with","slug":"llm-deliberation-evaluating-llms-with","title":"Cooperation, Competition, and Maliciousness: LLM-Stakeholders Interactive Negotiation","date":"2023-09-29","arxiv_id":"2309.17234","n_code_links":2,"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":["s-abdelnabi/llm-deliberation"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"multilingual-natural-language-processingmodel","title":"Multilingual Natural Language Processing Model for Radiology Reports -- The Summary is all you need!","date":"2023-09-29","arxiv_id":"2310.00100","n_code_links":0,"syntology":null},{"paper":"/paper/robots-that-can-see-leveraging-human-pose-for","slug":"robots-that-can-see-leveraging-human-pose-for","title":"Robots That Can See: Leveraging Human Pose for Trajectory Prediction","date":"2023-09-29","arxiv_id":"2309.17209","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":["google-research/human-scene-transformer"],"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":"/paper/scale-synergized-collaboration-of-asymmetric","slug":"scale-synergized-collaboration-of-asymmetric","title":"SCALE: Synergized Collaboration of Asymmetric Language Translation Engines","date":"2023-09-29","arxiv_id":"2309.17061","n_code_links":1,"syntology":null},{"paper":null,"slug":"scaling-experiments-in-self-supervised-cross","title":"Scaling Experiments in Self-Supervised Cross-Table Representation Learning","date":"2023-09-29","arxiv_id":"2309.17339","n_code_links":0,"syntology":null},{"paper":"/paper/socreval-large-language-models-with-the","slug":"socreval-large-language-models-with-the","title":"SocREval: Large Language Models with the Socratic Method for Reference-Free Reasoning Evaluation","date":"2023-09-29","arxiv_id":"2310.00074","n_code_links":1,"syntology":null},{"paper":null,"slug":"split-and-merge-aligning-position-biases-in","title":"Split and Merge: Aligning Position Biases in LLM-based Evaluators","date":"2023-09-29","arxiv_id":"2310.01432","n_code_links":0,"syntology":null},{"paper":"/paper/suspicion-agent-playing-imperfect-information","slug":"suspicion-agent-playing-imperfect-information","title":"Suspicion-Agent: Playing Imperfect Information Games with Theory of Mind Aware GPT-4","date":"2023-09-29","arxiv_id":"2309.17277","n_code_links":1,"syntology":{"ran":3,"of":4,"n_ran_checked":3,"n_instrument":0,"unverified":1,"pointer_only":4,"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":["cr-gjx/suspicion-agent"],"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/tora-a-tool-integrated-reasoning-agent-for","slug":"tora-a-tool-integrated-reasoning-agent-for","title":"ToRA: A Tool-Integrated Reasoning Agent for Mathematical Problem Solving","date":"2023-09-29","arxiv_id":"2309.17452","n_code_links":1,"syntology":{"ran":11,"of":12,"n_ran_checked":9,"n_instrument":2,"unverified":1,"pointer_only":9,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","official":{"repos":["microsoft/tora"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"trandrl-a-transformer-driven-deep","title":"TranDRL: A Transformer-Driven Deep Reinforcement Learning Enabled Prescriptive Maintenance Framework","date":"2023-09-29","arxiv_id":"2309.16935","n_code_links":0,"syntology":null},{"paper":"/paper/when-epipolar-constraint-meets-non-local-1","slug":"when-epipolar-constraint-meets-non-local-1","title":"When Epipolar Constraint Meets Non-local Operators in Multi-View Stereo","date":"2023-09-29","arxiv_id":"2309.17218","n_code_links":1,"syntology":{"ran":8,"of":13,"n_ran_checked":4,"n_instrument":4,"unverified":5,"pointer_only":0,"phrase":"8 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; 4 where Syntology's instrument failed) · 5 unverified","official":{"repos":["tqtqliu/et-mvsnet"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":5,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"ae-gpt-using-large-language-models-to-extract","title":"AE-GPT: Using Large Language Models to Extract Adverse Events from Surveillance Reports-A Use Case with Influenza Vaccine Adverse Events","date":"2023-09-28","arxiv_id":"2309.16150","n_code_links":0,"syntology":null},{"paper":"/paper/augmenting-transformers-with-recursively","slug":"augmenting-transformers-with-recursively","title":"Augmenting Transformers with Recursively Composed Multi-grained Representations","date":"2023-09-28","arxiv_id":"2309.16319","n_code_links":1,"syntology":{"ran":3,"of":3,"n_ran_checked":2,"n_instrument":1,"unverified":0,"pointer_only":0,"phrase":"3 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; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["ant-research/structuredlm_rtdt"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/channel-vision-transformers-an-image-is-worth","slug":"channel-vision-transformers-an-image-is-worth","title":"Channel Vision Transformers: An Image Is Worth 1 x 16 x 16 Words","date":"2023-09-28","arxiv_id":"2309.16108","n_code_links":1,"syntology":{"ran":4,"of":4,"n_ran_checked":2,"n_instrument":2,"unverified":0,"pointer_only":4,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 0 violated, 1 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","official":{"repos":["insitro/channelvit"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/controllable-text-generation-with-residual","slug":"controllable-text-generation-with-residual","title":"Controllable Text Generation with Residual Memory Transformer","date":"2023-09-28","arxiv_id":"2309.16231","n_code_links":2,"syntology":null},{"paper":"/paper/deep-geometrized-cartoon-line-inbetweening-1","slug":"deep-geometrized-cartoon-line-inbetweening-1","title":"Deep Geometrized Cartoon Line Inbetweening","date":"2023-09-28","arxiv_id":"2309.16643","n_code_links":1,"syntology":null},{"paper":"/paper/gaflow-incorporating-gaussian-attention-into-1","slug":"gaflow-incorporating-gaussian-attention-into-1","title":"GAFlow: Incorporating Gaussian Attention into Optical Flow","date":"2023-09-28","arxiv_id":"2309.16217","n_code_links":2,"syntology":{"ran":7,"of":9,"n_ran_checked":4,"n_instrument":3,"unverified":2,"pointer_only":9,"phrase":"7 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; 3 where Syntology's instrument failed) · 2 unverified","official":{"repos":["la30/gaflow"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":"/paper/gpt-fathom-benchmarking-large-language-models","slug":"gpt-fathom-benchmarking-large-language-models","title":"GPT-Fathom: Benchmarking Large Language Models to Decipher the Evolutionary Path towards GPT-4 and Beyond","date":"2023-09-28","arxiv_id":"2309.16583","n_code_links":1,"syntology":{"ran":5,"of":9,"n_ran_checked":5,"n_instrument":0,"unverified":4,"pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","official":{"repos":["gpt-fathom/gpt-fathom"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":4,"ran_from_kinds":["official"]}}},{"paper":"/paper/improving-equivariance-in-state-of-the-art-1","slug":"improving-equivariance-in-state-of-the-art-1","title":"Improving Equivariance in State-of-the-Art Supervised Depth and Normal Predictors","date":"2023-09-28","arxiv_id":"2309.16646","n_code_links":1,"syntology":null},{"paper":"/paper/lawbench-benchmarking-legal-knowledge-of","slug":"lawbench-benchmarking-legal-knowledge-of","title":"LawBench: Benchmarking Legal Knowledge of Large Language Models","date":"2023-09-28","arxiv_id":"2309.16289","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":["open-compass/lawbench"],"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":null,"slug":"multi-scale-recurrent-lstm-and-transformer","title":"Gated Cross-Attention Network for Depth Completion","date":"2023-09-28","arxiv_id":"2309.16301","n_code_links":0,"syntology":null},{"paper":null,"slug":"neuro-symbolic-reasoning-for-planning","title":"Neuro Symbolic Reasoning for Planning: Counterexample Guided Inductive Synthesis using Large Language Models and Satisfiability Solving","date":"2023-09-28","arxiv_id":"2309.16436","n_code_links":0,"syntology":null},{"paper":null,"slug":"radar-instance-transformer-reliable-moving","title":"Radar Instance Transformer: Reliable Moving Instance Segmentation in Sparse Radar Point Clouds","date":"2023-09-28","arxiv_id":"2309.16435","n_code_links":0,"syntology":null},{"paper":null,"slug":"stock-volatility-prediction-based-on","title":"Stock Volatility Prediction Based on Transformer Model Using Mixed-Frequency Data","date":"2023-09-28","arxiv_id":"2309.16196","n_code_links":0,"syntology":null},{"paper":null,"slug":"t1-t2-relaxation-temporal-modelling-from","title":"T1/T2 relaxation temporal modelling from accelerated acquisitions using a Latent Transformer","date":"2023-09-28","arxiv_id":"2309.16853","n_code_links":0,"syntology":null},{"paper":null,"slug":"uncertainty-aware-decision-transformer-for","title":"Uncertainty-Aware Decision Transformer for Stochastic Driving Environments","date":"2023-09-28","arxiv_id":"2309.16397","n_code_links":0,"syntology":null},{"paper":"/paper/unmasking-the-chameleons-a-benchmark-for-out","slug":"unmasking-the-chameleons-a-benchmark-for-out","title":"Unmasking the Chameleons: A Benchmark for Out-of-Distribution Detection in Medical Tabular Data","date":"2023-09-28","arxiv_id":"2309.16220","n_code_links":1,"syntology":{"ran":3,"of":4,"n_ran_checked":0,"n_instrument":3,"unverified":1,"pointer_only":0,"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) · 1 unverified","official":{"repos":["mazizmalayeri/tabmedood"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"upb-acti-detecting-conspiracies-using-fine","title":"UPB @ ACTI: Detecting Conspiracies using fine tuned Sentence Transformers","date":"2023-09-28","arxiv_id":"2309.16275","n_code_links":0,"syntology":null},{"paper":null,"slug":"uvl-a-unified-framework-for-video-tampering","title":"UVL2: A Unified Framework for Video Tampering Localization","date":"2023-09-28","arxiv_id":"2309.16126","n_code_links":0,"syntology":null},{"paper":"/paper/vision-transformers-need-registers","slug":"vision-transformers-need-registers","title":"Vision Transformers Need Registers","date":"2023-09-28","arxiv_id":"2309.16588","n_code_links":6,"syntology":{"ran":15,"of":20,"n_ran_checked":13,"n_instrument":2,"unverified":5,"pointer_only":2,"phrase":"15 ran (of which 0 constructed an object rather than computing a result; 13 with no instrument failure: 1 honoured, 1 violated, 11 with no contract checked; 2 where Syntology's instrument failed) · 5 unverified","official":{"repos":["facebookresearch/dinov2"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"paper":"/paper/aperture-diffraction-for-compact-snapshot-1","slug":"aperture-diffraction-for-compact-snapshot-1","title":"Aperture Diffraction for Compact Snapshot Spectral Imaging","date":"2023-09-27","arxiv_id":"2309.16372","n_code_links":1,"syntology":{"ran":1,"of":5,"n_ran_checked":1,"n_instrument":0,"unverified":4,"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) · 4 unverified","official":{"repos":["krito-ex/csst"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":4,"ran_from_kinds":["official"]}}},{"paper":"/paper/chatcounselor-a-large-language-models-for","slug":"chatcounselor-a-large-language-models-for","title":"ChatCounselor: A Large Language Models for Mental Health Support","date":"2023-09-27","arxiv_id":"2309.15461","n_code_links":1,"syntology":null},{"paper":null,"slug":"hpl-vit-a-unified-perception-framework-for","title":"HPL-ViT: A Unified Perception Framework for Heterogeneous Parallel LiDARs in V2V","date":"2023-09-27","arxiv_id":"2309.15572","n_code_links":0,"syntology":null},{"paper":"/paper/improving-facade-parsing-with-vision","slug":"improving-facade-parsing-with-vision","title":"Improving Facade Parsing with Vision Transformers and Line Integration","date":"2023-09-27","arxiv_id":"2309.15523","n_code_links":1,"syntology":null},{"paper":null,"slug":"neuromorphic-imaging-and-classification-with","title":"Neuromorphic Imaging and Classification with Graph Learning","date":"2023-09-27","arxiv_id":"2309.15627","n_code_links":0,"syntology":null},{"paper":"/paper/a-simple-text-to-video-model-via-transformer","slug":"a-simple-text-to-video-model-via-transformer","title":"A Simple Text to Video Model via Transformer","date":"2023-09-26","arxiv_id":"2309.14683","n_code_links":1,"syntology":null},{"paper":null,"slug":"balancing-computational-efficiency-and","title":"Balancing Computational Efficiency and Forecast Error in Machine Learning-based Time-Series Forecasting: Insights from Live Experiments on Meteorological Nowcasting","date":"2023-09-26","arxiv_id":"2309.15207","n_code_links":0,"syntology":null},{"paper":"/paper/benchmarking-large-language-models-on-cmexam-1","slug":"benchmarking-large-language-models-on-cmexam-1","title":"Benchmarking Large Language Models on CMExam - A comprehensive Chinese Medical Exam Dataset","date":"2023-09-26","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/capp-130-a-corpus-of-chinese-application","slug":"capp-130-a-corpus-of-chinese-application","title":"CAPP-130: A Corpus of Chinese Application Privacy Policy Summarization and Interpretation","date":"2023-09-26","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/event-stream-based-visual-object-tracking-a","slug":"event-stream-based-visual-object-tracking-a","title":"Event Stream-based Visual Object Tracking: A High-Resolution Benchmark Dataset and A Novel Baseline","date":"2023-09-26","arxiv_id":"2309.14611","n_code_links":4,"syntology":{"ran":9,"of":9,"n_ran_checked":9,"n_instrument":0,"unverified":0,"pointer_only":9,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 1 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["event-ahu/coesot","event-ahu/eventvot_benchmark","wangxiao5791509/Single_Object_Tracking_Paper_List","wangxiao5791509/VisEvent_SOT_Benchmark"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"leveraging-herpangina-data-to-enhance","title":"Leveraging Herpangina Data to Enhance Hospital-level Prediction of Hand-Foot-and-Mouth Disease Admissions Using UPTST","date":"2023-09-26","arxiv_id":"2309.14674","n_code_links":0,"syntology":null},{"paper":"/paper/question-answering-approach-to-evaluate-legal","slug":"question-answering-approach-to-evaluate-legal","title":"Question-Answering Approach to Evaluating Legal Summaries","date":"2023-09-26","arxiv_id":"2309.15016","n_code_links":1,"syntology":null},{"paper":"/paper/rankvicuna-zero-shot-listwise-document","slug":"rankvicuna-zero-shot-listwise-document","title":"RankVicuna: Zero-Shot Listwise Document Reranking with Open-Source Large Language Models","date":"2023-09-26","arxiv_id":"2309.15088","n_code_links":3,"syntology":null},{"paper":"/paper/robust-sequential-deepfake-detection","slug":"robust-sequential-deepfake-detection","title":"Robust Sequential DeepFake Detection","date":"2023-09-26","arxiv_id":"2309.14991","n_code_links":1,"syntology":null},{"paper":"/paper/supersonic-learning-to-generate-source-code","slug":"supersonic-learning-to-generate-source-code","title":"Supersonic: Learning to Generate Source Code Optimizations in C/C++","date":"2023-09-26","arxiv_id":"2309.14846","n_code_links":1,"syntology":null},{"paper":null,"slug":"tile-classification-based-viewport-prediction","title":"Tile Classification Based Viewport Prediction with Multi-modal Fusion Transformer","date":"2023-09-26","arxiv_id":"2309.14704","n_code_links":0,"syntology":null},{"paper":null,"slug":"visit-bench-a-dynamic-benchmark-for","title":"VisIT-Bench: A Dynamic Benchmark for Evaluating Instruction-Following Vision-and-Language Models","date":"2023-09-26","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/a-survey-of-transformer-applications-for","slug":"a-survey-of-transformer-applications-for","title":"A survey of Transformer applications for histopathological image analysis: New developments and future directions","date":"2023-09-25","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"aligning-large-multimodal-models-with","title":"Aligning Large Multimodal Models with Factually Augmented RLHF","date":"2023-09-25","arxiv_id":"2309.14525","n_code_links":0,"syntology":null},{"paper":"/paper/data-upcycling-knowledge-distillation-for","slug":"data-upcycling-knowledge-distillation-for","title":"Data Upcycling Knowledge Distillation for Image Super-Resolution","date":"2023-09-25","arxiv_id":"2309.14162","n_code_links":1,"syntology":null},{"paper":"/paper/deepspeed-ulysses-system-optimizations-for","slug":"deepspeed-ulysses-system-optimizations-for","title":"DeepSpeed Ulysses: System Optimizations for Enabling Training of Extreme Long Sequence Transformer Models","date":"2023-09-25","arxiv_id":"2309.14509","n_code_links":6,"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":null}},{"paper":"/paper/egocentric-rgb-depth-action-recognition-in","slug":"egocentric-rgb-depth-action-recognition-in","title":"Egocentric RGB+Depth Action Recognition in Industry-Like Settings","date":"2023-09-25","arxiv_id":"2309.13962","n_code_links":1,"syntology":null},{"paper":null,"slug":"evaluating-cognitive-maps-and-planning-in","title":"Evaluating Cognitive Maps and Planning in Large Language Models with CogEval","date":"2023-09-25","arxiv_id":"2309.15129","n_code_links":0,"syntology":null},{"paper":null,"slug":"guess-sketch-language-model-guided","title":"Guess & Sketch: Language Model Guided Transpilation","date":"2023-09-25","arxiv_id":"2309.14396","n_code_links":0,"syntology":null},{"paper":"/paper/loggpt-log-anomaly-detection-via-gpt","slug":"loggpt-log-anomaly-detection-via-gpt","title":"LogGPT: Log Anomaly Detection via GPT","date":"2023-09-25","arxiv_id":"2309.14482","n_code_links":1,"syntology":null},{"paper":null,"slug":"only-5-attention-is-all-you-need-efficient","title":"Only 5\\% Attention Is All You Need: Efficient Long-range Document-level Neural Machine Translation","date":"2023-09-25","arxiv_id":"2309.14174","n_code_links":0,"syntology":null},{"paper":null,"slug":"physics-of-language-models-part-3-2-knowledge","title":"Physics of Language Models: Part 3.2, Knowledge Manipulation","date":"2023-09-25","arxiv_id":"2309.14402","n_code_links":0,"syntology":null},{"paper":"/paper/unitedhuman-harnessing-multi-source-data-for","slug":"unitedhuman-harnessing-multi-source-data-for","title":"UnitedHuman: Harnessing Multi-Source Data for High-Resolution Human Generation","date":"2023-09-25","arxiv_id":"2309.14335","n_code_links":1,"syntology":null},{"paper":null,"slug":"watch-your-language-large-language-models-and","title":"Watch Your Language: Investigating Content Moderation with Large Language Models","date":"2023-09-25","arxiv_id":"2309.14517","n_code_links":0,"syntology":null},{"paper":null,"slug":"changes-aware-transformer-learning","title":"Changes-Aware Transformer: Learning Generalized Changes Representation","date":"2023-09-24","arxiv_id":"2309.13619","n_code_links":0,"syntology":null},{"paper":"/paper/global-correlated-3d-decoupling-transformer-1","slug":"global-correlated-3d-decoupling-transformer-1","title":"Global-correlated 3D-decoupling Transformer for Clothed Avatar Reconstruction","date":"2023-09-24","arxiv_id":"2309.13524","n_code_links":1,"syntology":{"ran":15,"of":18,"n_ran_checked":14,"n_instrument":1,"unverified":3,"pointer_only":18,"phrase":"15 ran (of which 0 constructed an object rather than computing a result; 14 with no instrument failure: 0 honoured, 0 violated, 14 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","official":{"repos":["river-zhang/gta"],"state":"official (archive's flag): 15 ran","n_ran":15,"n_constructed":0,"n_ran_no_instrument_failure":14,"n_unverified":3,"ran_from_kinds":["official"]}}},{"paper":"/paper/medivista-sam-zero-shot-medical-video","slug":"medivista-sam-zero-shot-medical-video","title":"MediViSTA: Medical Video Segmentation via Temporal Fusion SAM Adaptation for Echocardiography","date":"2023-09-24","arxiv_id":"2309.13539","n_code_links":1,"syntology":null},{"paper":null,"slug":"multi-dimensional-hyena-for-spatial-inductive","title":"Multi-Dimensional Hyena for Spatial Inductive Bias","date":"2023-09-24","arxiv_id":"2309.13600","n_code_links":0,"syntology":null},{"paper":null,"slug":"natural-language-based-context-modeling-and","title":"Natural Language based Context Modeling and Reasoning for Ubiquitous Computing with Large Language Models: A Tutorial","date":"2023-09-24","arxiv_id":"2309.15074","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-chat-about-boring-problems-studying-gpt","title":"A Chat About Boring Problems: Studying GPT-based text normalization","date":"2023-09-23","arxiv_id":"2309.13426","n_code_links":0,"syntology":null},{"paper":null,"slug":"algorithms-for-object-detection-in","title":"Algorithms for Object Detection in Substations","date":"2023-09-23","arxiv_id":"2311.07577","n_code_links":0,"syntology":null},{"paper":"/paper/asca-less-audio-data-is-more-insightful","slug":"asca-less-audio-data-is-more-insightful","title":"Asca: less audio data is more insightful","date":"2023-09-23","arxiv_id":"2309.13373","n_code_links":1,"syntology":null},{"paper":null,"slug":"attention-is-all-you-need-for-blind-room","title":"Attention Is All You Need For Blind Room Volume Estimation","date":"2023-09-23","arxiv_id":"2309.13504","n_code_links":0,"syntology":null},{"paper":null,"slug":"emgtfnet-fuzzy-vision-transformer-to-decode","title":"EMGTFNet: Fuzzy Vision Transformer to decode Upperlimb sEMG signals for Hand Gestures Recognition","date":"2023-09-23","arxiv_id":"2310.03754","n_code_links":0,"syntology":null},{"paper":null,"slug":"exploring-large-language-models-cognitive","title":"Probing the Moral Development of Large Language Models through Defining Issues Test","date":"2023-09-23","arxiv_id":"2309.13356","n_code_links":0,"syntology":null},{"paper":"/paper/glotscript-a-resource-and-tool-for-low","slug":"glotscript-a-resource-and-tool-for-low","title":"GlotScript: A Resource and Tool for Low Resource Writing System Identification","date":"2023-09-23","arxiv_id":"2309.13320","n_code_links":1,"syntology":{"ran":0,"of":2,"n_ran_checked":0,"n_instrument":0,"unverified":2,"pointer_only":0,"phrase":"0 ran · 2 unverified","official":{"repos":["cisnlp/GlotScript"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":[]}}},{"paper":null,"slug":"hindi-to-english-transformer-based-neural","title":"Hindi to English: Transformer-Based Neural Machine Translation","date":"2023-09-23","arxiv_id":"2309.13222","n_code_links":0,"syntology":null},{"paper":null,"slug":"randomize-to-generalize-domain-randomization","title":"Randomize to Generalize: Domain Randomization for Runway FOD Detection","date":"2023-09-23","arxiv_id":"2309.13264","n_code_links":0,"syntology":null},{"paper":null,"slug":"rbformer-improve-adversarial-robustness-of","title":"RBFormer: Improve Adversarial Robustness of Transformer by Robust Bias","date":"2023-09-23","arxiv_id":"2309.13245","n_code_links":0,"syntology":null},{"paper":"/paper/unihead-unifying-multi-perception-for","slug":"unihead-unifying-multi-perception-for","title":"UniHead: Unifying Multi-Perception for Detection Heads","date":"2023-09-23","arxiv_id":"2309.13242","n_code_links":1,"syntology":null},{"paper":"/paper/amplify-attention-based-mixup-for-performance","slug":"amplify-attention-based-mixup-for-performance","title":"AMPLIFY:Attention-based Mixup for Performance Improvement and Label Smoothing in Transformer","date":"2023-09-22","arxiv_id":"2309.12689","n_code_links":1,"syntology":null},{"paper":"/paper/associative-transformer-is-a-sparse","slug":"associative-transformer-is-a-sparse","title":"Associative Transformer","date":"2023-09-22","arxiv_id":"2309.12862","n_code_links":1,"syntology":null},{"paper":"/paper/clusterformer-clustering-as-a-universal","slug":"clusterformer-clustering-as-a-universal","title":"ClusterFormer: Clustering As A Universal Visual Learner","date":"2023-09-22","arxiv_id":"2309.13196","n_code_links":1,"syntology":null},{"paper":null,"slug":"deformer-integrating-transformers-with","title":"DeFormer: Integrating Transformers with Deformable Models for 3D Shape Abstraction from a Single Image","date":"2023-09-22","arxiv_id":"2309.12594","n_code_links":0,"syntology":null},{"paper":null,"slug":"durian-e-duration-informed-attention-network","title":"DurIAN-E: Duration Informed Attention Network For Expressive Text-to-Speech Synthesis","date":"2023-09-22","arxiv_id":"2309.12792","n_code_links":0,"syntology":null},{"paper":null,"slug":"modeling-spatiotemporal-periodicity-and","title":"Modeling Spatiotemporal Periodicity and Collaborative Signal for Local-Life Service Recommendation","date":"2023-09-22","arxiv_id":"2309.12565","n_code_links":0,"syntology":null},{"paper":"/paper/pointssc-a-cooperative-vehicle-infrastructure","slug":"pointssc-a-cooperative-vehicle-infrastructure","title":"PointSSC: A Cooperative Vehicle-Infrastructure Point Cloud Benchmark for Semantic Scene Completion","date":"2023-09-22","arxiv_id":"2309.12708","n_code_links":1,"syntology":{"ran":4,"of":4,"n_ran_checked":3,"n_instrument":1,"unverified":0,"pointer_only":4,"phrase":"4 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; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["yyxssm/pointssc"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/reconcile-round-table-conference-improves","slug":"reconcile-round-table-conference-improves","title":"ReConcile: Round-Table Conference Improves Reasoning via Consensus among Diverse LLMs","date":"2023-09-22","arxiv_id":"2309.13007","n_code_links":2,"syntology":{"ran":3,"of":3,"n_ran_checked":3,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["dinobby/reconcile"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"spion-layer-wise-sparse-training-of","title":"SPION: Layer-Wise Sparse Training of Transformer via Convolutional Flood Filling","date":"2023-09-22","arxiv_id":"2309.12578","n_code_links":0,"syntology":null},{"paper":"/paper/stylometrix-an-open-source-multilingual-tool","slug":"stylometrix-an-open-source-multilingual-tool","title":"StyloMetrix: An Open-Source Multilingual Tool for Representing Stylometric Vectors","date":"2023-09-22","arxiv_id":"2309.12810","n_code_links":1,"syntology":null},{"paper":null,"slug":"trtr-a-versatile-pre-trained-large-traffic","title":"TrTr: A Versatile Pre-Trained Large Traffic Model based on Transformer for Capturing Trajectory Diversity in Vehicle Population","date":"2023-09-22","arxiv_id":"2309.12677","n_code_links":0,"syntology":null},{"paper":null,"slug":"vision-transformers-for-computer-go","title":"Vision Transformers for Computer Go","date":"2023-09-22","arxiv_id":"2309.12675","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-robust-and-opponent-aware-league-training","title":"A Robust and Opponent-Aware League Training Method for StarCraft II","date":"2023-09-21","arxiv_id":null,"n_code_links":0,"syntology":null}],"record_sha256":"09c4d154d08276625604dd2c4873dbc935a34e5819704c125a3fc6dd0b284575","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}