{"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/80","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":80,"pages_in_order":144,"rows_per_page":100,"rows":[7901,8000],"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/79","next":"/method/label-smoothing/papers/81","papers":[{"paper":null,"slug":"large-multimodal-models-notes-on-cvpr-2023","title":"Large Multimodal Models: Notes on CVPR 2023 Tutorial","date":"2023-06-26","arxiv_id":"2306.14895","n_code_links":0,"syntology":null},{"paper":null,"slug":"lm4hpc-towards-effective-language-model","title":"LM4HPC: Towards Effective Language Model Application in High-Performance Computing","date":"2023-06-26","arxiv_id":"2306.14979","n_code_links":0,"syntology":null},{"paper":"/paper/longcoder-a-long-range-pre-trained-language","slug":"longcoder-a-long-range-pre-trained-language","title":"LongCoder: A Long-Range Pre-trained Language Model for Code Completion","date":"2023-06-26","arxiv_id":"2306.14893","n_code_links":1,"syntology":{"ran":1,"of":1,"n_ran_checked":0,"n_instrument":1,"unverified":0,"pointer_only":0,"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":["microsoft/CodeBERT"],"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":null,"slug":"parameternet-parameters-are-all-you-need-for","title":"ParameterNet: Parameters Are All You Need","date":"2023-06-26","arxiv_id":"2306.14525","n_code_links":0,"syntology":null},{"paper":null,"slug":"supervised-pretraining-can-learn-in-context","title":"Supervised Pretraining Can Learn In-Context Reinforcement Learning","date":"2023-06-26","arxiv_id":"2306.14892","n_code_links":0,"syntology":null},{"paper":"/paper/vint-a-foundation-model-for-visual-navigation","slug":"vint-a-foundation-model-for-visual-navigation","title":"ViNT: A Foundation Model for Visual Navigation","date":"2023-06-26","arxiv_id":"2306.14846","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-novel-dual-pooling-attention-module-for-uav","title":"A Novel Dual-pooling Attention Module for UAV Vehicle Re-identification","date":"2023-06-25","arxiv_id":"2306.14104","n_code_links":0,"syntology":null},{"paper":null,"slug":"adaptive-window-pruning-for-efficient-local","title":"Adaptive Window Pruning for Efficient Local Motion Deblurring","date":"2023-06-25","arxiv_id":"2306.14268","n_code_links":0,"syntology":null},{"paper":null,"slug":"g-sto-sequential-main-shopping-intention","title":"G-STO: Sequential Main Shopping Intention Detection via Graph-Regularized Stochastic Transformer","date":"2023-06-25","arxiv_id":"2306.14314","n_code_links":0,"syntology":null},{"paper":null,"slug":"multi-scale-cross-contrastive-learning-for","title":"Multi-Scale Cross Contrastive Learning for Semi-Supervised Medical Image Segmentation","date":"2023-06-25","arxiv_id":"2306.14293","n_code_links":0,"syntology":null},{"paper":null,"slug":"revolutionizing-cyber-threat-detection-with","title":"Revolutionizing Cyber Threat Detection with Large Language Models: A privacy-preserving BERT-based Lightweight Model for IoT/IIoT Devices","date":"2023-06-25","arxiv_id":"2306.14263","n_code_links":0,"syntology":null},{"paper":null,"slug":"steganographic-capacity-of-deep-learning","title":"Steganographic Capacity of Deep Learning Models","date":"2023-06-25","arxiv_id":"2306.17189","n_code_links":0,"syntology":null},{"paper":null,"slug":"action-q-transformer-visual-explanation-in","title":"Action Q-Transformer: Visual Explanation in Deep Reinforcement Learning with Encoder-Decoder Model using Action Query","date":"2023-06-24","arxiv_id":"2306.13879","n_code_links":0,"syntology":null},{"paper":null,"slug":"can-gpt-4-support-analysis-of-textual-data-in","title":"Can GPT-4 Support Analysis of Textual Data in Tasks Requiring Highly Specialized Domain Expertise?","date":"2023-06-24","arxiv_id":"2306.13906","n_code_links":0,"syntology":null},{"paper":null,"slug":"emotion-flip-reasoning-in-multiparty-1","title":"Emotion Flip Reasoning in Multiparty Conversations","date":"2023-06-24","arxiv_id":"2306.13959","n_code_links":0,"syntology":null},{"paper":"/paper/fusing-multimodal-signals-on-hyper-complex","slug":"fusing-multimodal-signals-on-hyper-complex","title":"Fusing Multimodal Signals on Hyper-complex Space for Extreme Abstractive Text Summarization (TL;DR) of Scientific Contents","date":"2023-06-24","arxiv_id":"2306.13968","n_code_links":1,"syntology":null},{"paper":null,"slug":"large-sequence-models-for-sequential-decision","title":"Large Sequence Models for Sequential Decision-Making: A Survey","date":"2023-06-24","arxiv_id":"2306.13945","n_code_links":0,"syntology":null},{"paper":null,"slug":"waypoint-transformer-reinforcement-learning","title":"Waypoint Transformer: Reinforcement Learning via Supervised Learning with Intermediate Targets","date":"2023-06-24","arxiv_id":"2306.14069","n_code_links":0,"syntology":null},{"paper":"/paper/bridging-the-performance-gap-between-detr-and","slug":"bridging-the-performance-gap-between-detr-and","title":"Bridging the Performance Gap between DETR and R-CNN for Graphical Object Detection in Document Images","date":"2023-06-23","arxiv_id":"2306.13526","n_code_links":0,"syntology":null},{"paper":null,"slug":"cross-language-speech-emotion-recognition","title":"Cross-Language Speech Emotion Recognition Using Multimodal Dual Attention Transformers","date":"2023-06-23","arxiv_id":"2306.13804","n_code_links":0,"syntology":null},{"paper":"/paper/efficient-online-processing-with-deep-neural","slug":"efficient-online-processing-with-deep-neural","title":"Efficient Online Processing with Deep Neural Networks","date":"2023-06-23","arxiv_id":"2306.13474","n_code_links":1,"syntology":null},{"paper":"/paper/incorporating-graph-information-in","slug":"incorporating-graph-information-in","title":"Incorporating Graph Information in Transformer-based AMR Parsing","date":"2023-06-23","arxiv_id":"2306.13467","n_code_links":1,"syntology":null},{"paper":"/paper/long-range-language-modeling-with-self","slug":"long-range-language-modeling-with-self","title":"Retrieval-Pretrained Transformer: Long-range Language Modeling with Self-retrieval","date":"2023-06-23","arxiv_id":"2306.13421","n_code_links":1,"syntology":{"ran":4,"of":4,"n_ran_checked":0,"n_instrument":4,"unverified":0,"pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 4 where Syntology's instrument failed) · 0 unverified","official":{"repos":["ohadrubin/rpt"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/prores-exploring-degradation-aware-visual","slug":"prores-exploring-degradation-aware-visual","title":"ProRes: Exploring Degradation-aware Visual Prompt for Universal Image Restoration","date":"2023-06-23","arxiv_id":"2306.13653","n_code_links":1,"syntology":{"ran":2,"of":2,"n_ran_checked":0,"n_instrument":2,"unverified":0,"pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","official":{"repos":["leonmakise/prores"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"swin-free-achieving-better-cross-window","title":"Swin-Free: Achieving Better Cross-Window Attention and Efficiency with Size-varying Window","date":"2023-06-23","arxiv_id":"2306.13776","n_code_links":0,"syntology":null},{"paper":null,"slug":"the-double-helix-inside-the-nlp-transformer","title":"The Double Helix inside the NLP Transformer","date":"2023-06-23","arxiv_id":"2306.13817","n_code_links":0,"syntology":null},{"paper":null,"slug":"upscaling-global-hourly-gpp-with-temporal","title":"Upscaling Global Hourly GPP with Temporal Fusion Transformer (TFT)","date":"2023-06-23","arxiv_id":"2306.13815","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-comparison-of-time-based-models-for","title":"A Comparison of Time-based Models for Multimodal Emotion Recognition","date":"2023-06-22","arxiv_id":"2306.13076","n_code_links":0,"syntology":null},{"paper":"/paper/can-llms-express-their-uncertainty-an","slug":"can-llms-express-their-uncertainty-an","title":"Can LLMs Express Their Uncertainty? An Empirical Evaluation of Confidence Elicitation in LLMs","date":"2023-06-22","arxiv_id":"2306.13063","n_code_links":1,"syntology":{"ran":3,"of":4,"n_ran_checked":0,"n_instrument":3,"unverified":1,"pointer_only":4,"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":["miaoxiong2320/llm-uncertainty"],"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":"/paper/cross-lingual-cross-temporal-summarization","slug":"cross-lingual-cross-temporal-summarization","title":"Cross-lingual Cross-temporal Summarization: Dataset, Models, Evaluation","date":"2023-06-22","arxiv_id":"2306.12916","n_code_links":1,"syntology":null},{"paper":null,"slug":"deep-metric-learning-with-soft-orthogonal","title":"Deep Metric Learning with Soft Orthogonal Proxies","date":"2023-06-22","arxiv_id":"2306.13055","n_code_links":0,"syntology":null},{"paper":null,"slug":"learning-from-visual-observation-via-offline-1","title":"Learning from Visual Observation via Offline Pretrained State-to-Go Transformer","date":"2023-06-22","arxiv_id":"2306.12860","n_code_links":0,"syntology":null},{"paper":"/paper/minimalist-and-high-quality-panoramic-imaging","slug":"minimalist-and-high-quality-panoramic-imaging","title":"Minimalist and High-Quality Panoramic Imaging with PSF-aware Transformers","date":"2023-06-22","arxiv_id":"2306.12992","n_code_links":1,"syntology":null},{"paper":"/paper/visual-adversarial-examples-jailbreak-large","slug":"visual-adversarial-examples-jailbreak-large","title":"Visual Adversarial Examples Jailbreak Aligned Large Language Models","date":"2023-06-22","arxiv_id":"2306.13213","n_code_links":1,"syntology":null},{"paper":"/paper/aries-a-corpus-of-scientific-paper-edits-made","slug":"aries-a-corpus-of-scientific-paper-edits-made","title":"ARIES: A Corpus of Scientific Paper Edits Made in Response to Peer Reviews","date":"2023-06-21","arxiv_id":"2306.12587","n_code_links":1,"syntology":{"ran":5,"of":8,"n_ran_checked":5,"n_instrument":0,"unverified":3,"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) · 3 unverified","official":{"repos":["allenai/aries"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":3,"ran_from_kinds":["official"]}}},{"paper":"/paper/deep-language-networks-joint-prompt-training","slug":"deep-language-networks-joint-prompt-training","title":"Joint Prompt Optimization of Stacked LLMs using Variational Inference","date":"2023-06-21","arxiv_id":"2306.12509","n_code_links":1,"syntology":{"ran":3,"of":19,"n_ran_checked":0,"n_instrument":3,"unverified":16,"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) · 16 unverified","official":{"repos":["microsoft/deep-language-networks"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":16,"ran_from_kinds":["official"]}}},{"paper":"/paper/fast-segment-anything","slug":"fast-segment-anything","title":"Fast Segment Anything","date":"2023-06-21","arxiv_id":"2306.12156","n_code_links":1,"syntology":null},{"paper":null,"slug":"gpt-based-models-meet-simulation-how-to","title":"GPT-Based Models Meet Simulation: How to Efficiently Use Large-Scale Pre-Trained Language Models Across Simulation Tasks","date":"2023-06-21","arxiv_id":"2306.13679","n_code_links":0,"syntology":null},{"paper":null,"slug":"hsr-diff-hyperspectral-image-super-resolution","title":"HSR-Diff:Hyperspectral Image Super-Resolution via Conditional Diffusion Models","date":"2023-06-21","arxiv_id":"2306.12085","n_code_links":0,"syntology":null},{"paper":"/paper/inter-instance-similarity-modeling-for","slug":"inter-instance-similarity-modeling-for","title":"Inter-Instance Similarity Modeling for Contrastive Learning","date":"2023-06-21","arxiv_id":"2306.12243","n_code_links":1,"syntology":null},{"paper":null,"slug":"msw-transformer-multi-scale-shifted-windows","title":"MSW-Transformer: Multi-Scale Shifted Windows Transformer Networks for 12-Lead ECG Classification","date":"2023-06-21","arxiv_id":"2306.12098","n_code_links":0,"syntology":null},{"paper":"/paper/neural-multigrid-memory-for-computational","slug":"neural-multigrid-memory-for-computational","title":"Neural Multigrid Memory For Computational Fluid Dynamics","date":"2023-06-21","arxiv_id":"2306.12545","n_code_links":1,"syntology":null},{"paper":null,"slug":"probing-the-limit-of-hydrologic","title":"Probing the limit of hydrologic predictability with the Transformer network","date":"2023-06-21","arxiv_id":"2306.12384","n_code_links":0,"syntology":null},{"paper":"/paper/starvqa-co-training-space-time-attention-for","slug":"starvqa-co-training-space-time-attention-for","title":"StarVQA+: Co-training Space-Time Attention for Video Quality Assessment","date":"2023-06-21","arxiv_id":"2306.12298","n_code_links":1,"syntology":null},{"paper":"/paper/what-constitutes-good-contrastive-learning-in","slug":"what-constitutes-good-contrastive-learning-in","title":"What Constitutes Good Contrastive Learning in Time-Series Forecasting?","date":"2023-06-21","arxiv_id":"2306.12086","n_code_links":1,"syntology":{"ran":5,"of":5,"n_ran_checked":5,"n_instrument":0,"unverified":0,"pointer_only":1,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 1 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["chiyuzhang94/contrastive_learning_time-series_e2e"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"comparing-deep-learning-models-for-volatility","title":"Comparing Deep Learning Models for the Task of Volatility Prediction Using Multivariate Data","date":"2023-06-20","arxiv_id":"2306.12446","n_code_links":0,"syntology":null},{"paper":null,"slug":"decodingtrust-a-comprehensive-assessment-of","title":"DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models","date":"2023-06-20","arxiv_id":"2306.11698","n_code_links":0,"syntology":null},{"paper":null,"slug":"democratizing-llms-for-low-resource-languages","title":"Democratizing LLMs for Low-Resource Languages by Leveraging their English Dominant Abilities with Linguistically-Diverse Prompts","date":"2023-06-20","arxiv_id":"2306.11372","n_code_links":0,"syntology":null},{"paper":"/paper/gpt-4-reticular-chemist-for-mof-discovery","slug":"gpt-4-reticular-chemist-for-mof-discovery","title":"A GPT-4 Reticular Chemist for Guiding MOF Discovery","date":"2023-06-20","arxiv_id":"2306.14915","n_code_links":1,"syntology":null},{"paper":null,"slug":"harnessing-the-power-of-adversarial-prompting","title":"Harnessing the Power of Adversarial Prompting and Large Language Models for Robust Hypothesis Generation in Astronomy","date":"2023-06-20","arxiv_id":"2306.11648","n_code_links":0,"syntology":null},{"paper":"/paper/learning-to-generate-better-than-your-llm","slug":"learning-to-generate-better-than-your-llm","title":"Learning to Generate Better Than Your LLM","date":"2023-06-20","arxiv_id":"2306.11816","n_code_links":1,"syntology":null},{"paper":null,"slug":"multiverse-transformer-1st-place-solution-for","title":"Multiverse Transformer: 1st Place Solution for Waymo Open Sim Agents Challenge 2023","date":"2023-06-20","arxiv_id":"2306.11868","n_code_links":0,"syntology":null},{"paper":null,"slug":"rm-prt-realistic-robotic-manipulation","title":"Surfer: Progressive Reasoning with World Models for Robotic Manipulation","date":"2023-06-20","arxiv_id":"2306.11335","n_code_links":0,"syntology":null},{"paper":null,"slug":"transforming-graphs-for-enhanced-attribute","title":"Transforming Graphs for Enhanced Attribute Clustering: An Innovative Graph Transformer-Based Method","date":"2023-06-20","arxiv_id":"2306.11307","n_code_links":0,"syntology":null},{"paper":null,"slug":"unfolding-framework-with-prior-of-convolution","title":"Unfolding Framework with Prior of Convolution-Transformer Mixture and Uncertainty Estimation for Video Snapshot Compressive Imaging","date":"2023-06-20","arxiv_id":"2306.11316","n_code_links":0,"syntology":null},{"paper":"/paper/amrs-assemble-learning-to-ensemble-with","slug":"amrs-assemble-learning-to-ensemble-with","title":"AMRs Assemble! Learning to Ensemble with Autoregressive Models for AMR Parsing","date":"2023-06-19","arxiv_id":"2306.10786","n_code_links":1,"syntology":null},{"paper":"/paper/bayling-bridging-cross-lingual-alignment-and","slug":"bayling-bridging-cross-lingual-alignment-and","title":"BayLing: Bridging Cross-lingual Alignment and Instruction Following through Interactive Translation for Large Language Models","date":"2023-06-19","arxiv_id":"2306.10968","n_code_links":1,"syntology":null},{"paper":"/paper/multi-task-learning-for-radar-signal","slug":"multi-task-learning-for-radar-signal","title":"Multi-task Learning for Radar Signal Characterisation","date":"2023-06-19","arxiv_id":"2306.13105","n_code_links":1,"syntology":null},{"paper":null,"slug":"multitrack-music-transcription-with-a-time","title":"Multitrack Music Transcription with a Time-Frequency Perceiver","date":"2023-06-19","arxiv_id":"2306.10785","n_code_links":0,"syntology":null},{"paper":"/paper/nar-former-v2-rethinking-transformer-for-1","slug":"nar-former-v2-rethinking-transformer-for-1","title":"NAR-Former V2: Rethinking Transformer for Universal Neural Network Representation Learning","date":"2023-06-19","arxiv_id":"2306.10792","n_code_links":1,"syntology":{"ran":9,"of":15,"n_ran_checked":9,"n_instrument":0,"unverified":6,"pointer_only":0,"phrase":"9 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; 0 where Syntology's instrument failed) · 6 unverified","official":{"repos":["yuny220/NAR-Former-V2"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":6,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"ravitt-random-vision-transformer-tokens","title":"RaViTT: Random Vision Transformer Tokens","date":"2023-06-19","arxiv_id":"2306.10959","n_code_links":0,"syntology":null},{"paper":"/paper/road-barlow-twins-redundancy-reduction-for","slug":"road-barlow-twins-redundancy-reduction-for","title":"RedMotion: Motion Prediction via Redundancy Reduction","date":"2023-06-19","arxiv_id":"2306.10840","n_code_links":3,"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":["kit-mrt/red-motion","kit-mrt/road-barlow-twins","kit-mrt/future-motion"],"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":null,"slug":"temporal-data-meets-llm-explainable-financial","title":"Temporal Data Meets LLM -- Explainable Financial Time Series Forecasting","date":"2023-06-19","arxiv_id":"2306.11025","n_code_links":0,"syntology":null},{"paper":"/paper/transformer-training-strategies-for","slug":"transformer-training-strategies-for","title":"Transformer Training Strategies for Forecasting Multiple Load Time Series","date":"2023-06-19","arxiv_id":"2306.10891","n_code_links":1,"syntology":null},{"paper":"/paper/enhanced-masked-image-modeling-for-analysis","slug":"enhanced-masked-image-modeling-for-analysis","title":"Enhanced Masked Image Modeling for Analysis of Dental Panoramic Radiographs","date":"2023-06-18","arxiv_id":"2306.10623","n_code_links":1,"syntology":null},{"paper":null,"slug":"gender-bias-in-transformer-models-a","title":"Gender Bias in Transformer Models: A comprehensive survey","date":"2023-06-18","arxiv_id":"2306.10530","n_code_links":0,"syntology":null},{"paper":null,"slug":"mixed-curvature-transformers-for-graph","title":"Mixed-Curvature Transformers for Graph Representation Learning papersreview","date":"2023-06-18","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"news-verifiers-showdown-a-comparative","title":"News Verifiers Showdown: A Comparative Performance Evaluation of ChatGPT 3.5, ChatGPT 4.0, Bing AI, and Bard in News Fact-Checking","date":"2023-06-18","arxiv_id":"2306.17176","n_code_links":0,"syntology":null},{"paper":null,"slug":"snowman-a-million-scale-chinese-commonsense","title":"Snowman: A Million-scale Chinese Commonsense Knowledge Graph Distilled from Foundation Model","date":"2023-06-17","arxiv_id":"2306.10241","n_code_links":0,"syntology":null},{"paper":null,"slug":"ad-autogpt-an-autonomous-gpt-for-alzheimer-s","title":"AD-AutoGPT: An Autonomous GPT for Alzheimer's Disease Infodemiology","date":"2023-06-16","arxiv_id":"2306.10095","n_code_links":0,"syntology":null},{"paper":"/paper/demystifying-gpt-self-repair-for-code","slug":"demystifying-gpt-self-repair-for-code","title":"Is Self-Repair a Silver Bullet for Code Generation?","date":"2023-06-16","arxiv_id":"2306.09896","n_code_links":1,"syntology":null},{"paper":"/paper/end-to-end-vectorized-hd-map-construction-1","slug":"end-to-end-vectorized-hd-map-construction-1","title":"End-to-End Vectorized HD-map Construction with Piecewise Bezier Curve","date":"2023-06-16","arxiv_id":"2306.09700","n_code_links":1,"syntology":null},{"paper":"/paper/evaluating-superhuman-models-with-consistency","slug":"evaluating-superhuman-models-with-consistency","title":"Evaluating Superhuman Models with Consistency Checks","date":"2023-06-16","arxiv_id":"2306.09983","n_code_links":2,"syntology":null},{"paper":null,"slug":"investigating-masking-based-data-generation","title":"Investigating Masking-based Data Generation in Language Models","date":"2023-06-16","arxiv_id":"2307.00008","n_code_links":0,"syntology":null},{"paper":"/paper/multiwave-multiresolution-deep-architectures","slug":"multiwave-multiresolution-deep-architectures","title":"MultiWave: Multiresolution Deep Architectures through Wavelet Decomposition for Multivariate Time Series Prediction","date":"2023-06-16","arxiv_id":"2306.10164","n_code_links":1,"syntology":null},{"paper":null,"slug":"robot-learning-with-sensorimotor-pre-training","title":"Robot Learning with Sensorimotor Pre-training","date":"2023-06-16","arxiv_id":"2306.10007","n_code_links":0,"syntology":null},{"paper":null,"slug":"tsnet-sac-leveraging-transformers-for","title":"TSNet-SAC: Leveraging Transformers for Efficient Task Scheduling","date":"2023-06-16","arxiv_id":"2307.07445","n_code_links":0,"syntology":null},{"paper":null,"slug":"block-state-transformer","title":"Block-State Transformers","date":"2023-06-15","arxiv_id":"2306.09539","n_code_links":0,"syntology":null},{"paper":"/paper/efficient-token-guided-image-text-retrieval","slug":"efficient-token-guided-image-text-retrieval","title":"Efficient Token-Guided Image-Text Retrieval with Consistent Multimodal Contrastive Training","date":"2023-06-15","arxiv_id":"2306.08789","n_code_links":1,"syntology":{"ran":11,"of":15,"n_ran_checked":10,"n_instrument":1,"unverified":4,"pointer_only":2,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 0 honoured, 1 violated, 9 with no contract checked; 1 where Syntology's instrument failed) · 4 unverified","official":null}},{"paper":null,"slug":"explaining-legal-concepts-with-augmented","title":"Explaining Legal Concepts with Augmented Large Language Models (GPT-4)","date":"2023-06-15","arxiv_id":"2306.09525","n_code_links":0,"syntology":null},{"paper":null,"slug":"exploring-the-mit-mathematics-and-eecs","title":"Exploring the MIT Mathematics and EECS Curriculum Using Large Language Models","date":"2023-06-15","arxiv_id":"2306.08997","n_code_links":0,"syntology":null},{"paper":"/paper/fast-training-of-diffusion-models-with-masked","slug":"fast-training-of-diffusion-models-with-masked","title":"Fast Training of Diffusion Models with Masked Transformers","date":"2023-06-15","arxiv_id":"2306.09305","n_code_links":1,"syntology":{"ran":13,"of":17,"n_ran_checked":10,"n_instrument":3,"unverified":4,"pointer_only":2,"phrase":"13 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 1 honoured, 0 violated, 9 with no contract checked; 3 where Syntology's instrument failed) · 4 unverified","official":{"repos":["anima-lab/maskdit"],"state":"official (archive's flag): 13 ran","n_ran":13,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":4,"ran_from_kinds":["official"]}}},{"paper":"/paper/recurrent-memory-decision-transformer","slug":"recurrent-memory-decision-transformer","title":"Recurrent Action Transformer with Memory","date":"2023-06-15","arxiv_id":"2306.09459","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 0 constructed an object rather than computing a result; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["airi-institute/rate"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"relational-temporal-graph-reasoning-for-dual","title":"Relational Temporal Graph Reasoning for Dual-task Dialogue Language Understanding","date":"2023-06-15","arxiv_id":"2306.09114","n_code_links":0,"syntology":null},{"paper":null,"slug":"revealing-the-illusion-of-joint-multimodal","title":"Dissecting Multimodality in VideoQA Transformer Models by Impairing Modality Fusion","date":"2023-06-15","arxiv_id":"2306.08889","n_code_links":0,"syntology":null},{"paper":"/paper/seeing-the-pose-in-the-pixels-learning-pose","slug":"seeing-the-pose-in-the-pixels-learning-pose","title":"Seeing the Pose in the Pixels: Learning Pose-Aware Representations in Vision Transformers","date":"2023-06-15","arxiv_id":"2306.09331","n_code_links":1,"syntology":null},{"paper":"/paper/slamb-accelerated-large-batch-training-with","slug":"slamb-accelerated-large-batch-training-with","title":"SLAMB: Accelerated Large Batch Training with Sparse Communication","date":"2023-06-15","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"team-acielee-technical-report-for-epic-sounds","title":"Team AcieLee: Technical Report for EPIC-SOUNDS Audio-Based Interaction Recognition Challenge 2023","date":"2023-06-15","arxiv_id":"2306.08998","n_code_links":0,"syntology":null},{"paper":null,"slug":"thrilled-by-your-progress-large-language","title":"Thrilled by Your Progress! Large Language Models (GPT-4) No Longer Struggle to Pass Assessments in Higher Education Programming Courses","date":"2023-06-15","arxiv_id":"2306.10073","n_code_links":0,"syntology":null},{"paper":null,"slug":"tighter-prediction-intervals-for-causal","title":"Ensembled Prediction Intervals for Causal Outcomes Under Hidden Confounding","date":"2023-06-15","arxiv_id":"2306.09520","n_code_links":0,"syntology":null},{"paper":null,"slug":"training-diffusion-classifiers-with-denoising","title":"DiffAug: A Diffuse-and-Denoise Augmentation for Training Robust Classifiers","date":"2023-06-15","arxiv_id":"2306.09192","n_code_links":0,"syntology":null},{"paper":null,"slug":"m-2unet-metaformer-multi-scale-upsampling","title":"M^2UNet: MetaFormer Multi-scale Upsampling Network for Polyp Segmentation","date":"2023-06-14","arxiv_id":"2306.08600","n_code_links":0,"syntology":null},{"paper":null,"slug":"mcr-data2vec-2-0-improving-self-supervised","title":"MCR-Data2vec 2.0: Improving Self-supervised Speech Pre-training via Model-level Consistency Regularization","date":"2023-06-14","arxiv_id":"2306.08463","n_code_links":0,"syntology":null},{"paper":"/paper/muben-benchmarking-the-uncertainty-of-pre","slug":"muben-benchmarking-the-uncertainty-of-pre","title":"MUBen: Benchmarking the Uncertainty of Molecular Representation Models","date":"2023-06-14","arxiv_id":"2306.10060","n_code_links":2,"syntology":{"ran":1,"of":2,"n_ran_checked":0,"n_instrument":1,"unverified":1,"pointer_only":0,"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":["Yinghao-Li/UncertaintyBenchmark","yinghao-li/muben"],"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":"/paper/multimodal-optimal-transport-based-co","slug":"multimodal-optimal-transport-based-co","title":"Multimodal Optimal Transport-based Co-Attention Transformer with Global Structure Consistency for Survival Prediction","date":"2023-06-14","arxiv_id":"2306.08330","n_code_links":3,"syntology":{"ran":10,"of":18,"n_ran_checked":7,"n_instrument":3,"unverified":8,"pointer_only":18,"phrase":"10 ran (of which 6 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 3 where Syntology's instrument failed) · 8 unverified","official":{"repos":["innse/motcat"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":["listed"]}}},{"paper":null,"slug":"research-on-named-entity-recognition-in","title":"Research on Named Entity Recognition in Improved transformer with R-Drop structure","date":"2023-06-14","arxiv_id":"2306.08315","n_code_links":0,"syntology":null},{"paper":"/paper/the-elm-neuron-an-efficient-and-expressive","slug":"the-elm-neuron-an-efficient-and-expressive","title":"The Expressive Leaky Memory Neuron: an Efficient and Expressive Phenomenological Neuron Model Can Solve Long-Horizon Tasks","date":"2023-06-14","arxiv_id":"2306.16922","n_code_links":1,"syntology":{"ran":6,"of":7,"n_ran_checked":3,"n_instrument":3,"unverified":1,"pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 2 honoured, 0 violated, 1 with no contract checked; 3 where Syntology's instrument failed) · 1 unverified","official":{"repos":["AaronSpieler/elmneuron"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":"/paper/tsmixer-lightweight-mlp-mixer-model-for","slug":"tsmixer-lightweight-mlp-mixer-model-for","title":"TSMixer: Lightweight MLP-Mixer Model for Multivariate Time Series Forecasting","date":"2023-06-14","arxiv_id":"2306.09364","n_code_links":1,"syntology":{"ran":7,"of":8,"n_ran_checked":7,"n_instrument":0,"unverified":1,"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) · 1 unverified","official":{"repos":["ibm/tsfm"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"unraveling-the-arc-puzzle-mimicking-human","title":"Unraveling the ARC Puzzle: Mimicking Human Solutions with Object-Centric Decision Transformer","date":"2023-06-14","arxiv_id":"2306.08204","n_code_links":0,"syntology":null},{"paper":"/paper/when-to-use-efficient-self-attention","slug":"when-to-use-efficient-self-attention","title":"When to Use Efficient Self Attention? Profiling Text, Speech and Image Transformer Variants","date":"2023-06-14","arxiv_id":"2306.08667","n_code_links":1,"syntology":null}],"record_sha256":"22b74ef4f8a795d65eea1455885cd53c9a40140ebcf2bf9f0a32e1a57455abd8","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}