{"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/66","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":66,"pages_in_order":144,"rows_per_page":100,"rows":[6501,6600],"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/65","next":"/method/label-smoothing/papers/67","papers":[{"paper":null,"slug":"brainnetdiff-generative-ai-empowers-brain","title":"BrainNetDiff: Generative AI Empowers Brain Network Generation via Multimodal Diffusion Model","date":"2023-11-09","arxiv_id":"2311.05199","n_code_links":0,"syntology":null},{"paper":"/paper/conic10k-a-challenging-math-problem","slug":"conic10k-a-challenging-math-problem","title":"Conic10K: A Challenging Math Problem Understanding and Reasoning Dataset","date":"2023-11-09","arxiv_id":"2311.05113","n_code_links":1,"syntology":null},{"paper":null,"slug":"deep-learning-in-computed-tomography","title":"Deep learning in computed tomography pulmonary angiography imaging: a dual-pronged approach for pulmonary embolism detection","date":"2023-11-09","arxiv_id":"2311.05197","n_code_links":0,"syntology":null},{"paper":null,"slug":"do-personality-tests-generalize-to-large","title":"Challenging the Validity of Personality Tests for Large Language Models","date":"2023-11-09","arxiv_id":"2311.05297","n_code_links":0,"syntology":null},{"paper":null,"slug":"dynamic-association-learning-of-self","title":"Dynamic Association Learning of Self-Attention and Convolution in Image Restoration","date":"2023-11-09","arxiv_id":"2311.05147","n_code_links":0,"syntology":null},{"paper":null,"slug":"intelligent-cervical-spine-fracture-detection","title":"Intelligent Cervical Spine Fracture Detection Using Deep Learning Methods","date":"2023-11-09","arxiv_id":"2311.05708","n_code_links":0,"syntology":null},{"paper":null,"slug":"protein-ligand-binding-representation","title":"Protein-ligand binding representation learning from fine-grained interactions","date":"2023-11-09","arxiv_id":"2311.16160","n_code_links":0,"syntology":null},{"paper":"/paper/technical-report-large-language-models-can","slug":"technical-report-large-language-models-can","title":"Large Language Models can Strategically Deceive their Users when Put Under Pressure","date":"2023-11-09","arxiv_id":"2311.07590","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":["apolloresearch/insider-trading"],"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/vision-encoder-decoder-models-for-ai-coaching","slug":"vision-encoder-decoder-models-for-ai-coaching","title":"Vision Encoder-Decoder Models for AI Coaching","date":"2023-11-09","arxiv_id":"2311.16161","n_code_links":2,"syntology":null},{"paper":"/paper/beyond-size-how-gradients-shape-pruning","slug":"beyond-size-how-gradients-shape-pruning","title":"Beyond Size: How Gradients Shape Pruning Decisions in Large Language Models","date":"2023-11-08","arxiv_id":"2311.04902","n_code_links":2,"syntology":{"ran":3,"of":7,"n_ran_checked":2,"n_instrument":1,"unverified":4,"pointer_only":1,"phrase":"3 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; 1 where Syntology's instrument failed) · 4 unverified","official":{"repos":["rocktimjyotidas/gblm-pruner","vila-lab/gblm-pruner"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":4,"ran_from_kinds":["official"]}}},{"paper":"/paper/data-factors-for-better-compositional","slug":"data-factors-for-better-compositional","title":"Data Factors for Better Compositional Generalization","date":"2023-11-08","arxiv_id":"2311.04420","n_code_links":1,"syntology":{"ran":8,"of":8,"n_ran_checked":8,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["owenzx/data4comp"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/euclidean-projective-conformal-choosing-a","slug":"euclidean-projective-conformal-choosing-a","title":"Euclidean, Projective, Conformal: Choosing a Geometric Algebra for Equivariant Transformers","date":"2023-11-08","arxiv_id":"2311.04744","n_code_links":1,"syntology":{"ran":1,"of":1,"n_ran_checked":1,"n_instrument":0,"unverified":0,"pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":null}},{"paper":"/paper/fibrovit-vision-transformer-based-framework","slug":"fibrovit-vision-transformer-based-framework","title":"FibroVit—Vision transformer-based framework for detection and classification of pulmonary fibrosis from chest CT images","date":"2023-11-08","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"hybrid-focal-and-full-range-attention-based","title":"Hybrid Focal and Full-Range Attention Based Graph Transformers","date":"2023-11-08","arxiv_id":"2311.04653","n_code_links":0,"syntology":null},{"paper":"/paper/loss-masking-is-not-needed-in-decoder-only","slug":"loss-masking-is-not-needed-in-decoder-only","title":"Loss Masking Is Not Needed in Decoder-only Transformer for Discrete-token-based ASR","date":"2023-11-08","arxiv_id":"2311.04534","n_code_links":1,"syntology":null},{"paper":"/paper/rethinking-benchmark-and-contamination-for","slug":"rethinking-benchmark-and-contamination-for","title":"Rethinking Benchmark and Contamination for Language Models with Rephrased Samples","date":"2023-11-08","arxiv_id":"2311.04850","n_code_links":1,"syntology":{"ran":3,"of":3,"n_ran_checked":0,"n_instrument":3,"unverified":0,"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) · 0 unverified","official":{"repos":["lm-sys/llm-decontaminator"],"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":"/paper/ss-mae-spatial-spectral-masked-auto-encoder","slug":"ss-mae-spatial-spectral-masked-auto-encoder","title":"SS-MAE: Spatial-Spectral Masked Auto-Encoder for Multi-Source Remote Sensing Image Classification","date":"2023-11-08","arxiv_id":"2311.04442","n_code_links":1,"syntology":{"ran":9,"of":15,"n_ran_checked":7,"n_instrument":2,"unverified":6,"pointer_only":15,"phrase":"9 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; 2 where Syntology's instrument failed) · 6 unverified","official":{"repos":["summitgao/ss-mae"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":6,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"towards-few-annotation-learning-in-computer","title":"Towards Few-Annotation Learning in Computer Vision: Application to Image Classification and Object Detection tasks","date":"2023-11-08","arxiv_id":"2311.04888","n_code_links":0,"syntology":null},{"paper":null,"slug":"vital-sign-forecasting-for-sepsis-patients-in","title":"Vital Sign Forecasting for Sepsis Patients in ICUs","date":"2023-11-08","arxiv_id":"2311.04770","n_code_links":0,"syntology":null},{"paper":"/paper/a-simple-interpretable-transformer-for-fine","slug":"a-simple-interpretable-transformer-for-fine","title":"A Simple Interpretable Transformer for Fine-Grained Image Classification and Analysis","date":"2023-11-07","arxiv_id":"2311.04157","n_code_links":1,"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":["imageomics/intr"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"analysis-of-the-user-perception-of-chatbots","title":"Analysis of the User Perception of Chatbots in Education Using A Partial Least Squares Structural Equation Modeling Approach","date":"2023-11-07","arxiv_id":"2311.03636","n_code_links":0,"syntology":null},{"paper":"/paper/black-box-prompt-optimization-aligning-large","slug":"black-box-prompt-optimization-aligning-large","title":"Black-Box Prompt Optimization: Aligning Large Language Models without Model Training","date":"2023-11-07","arxiv_id":"2311.04155","n_code_links":1,"syntology":{"ran":5,"of":7,"n_ran_checked":5,"n_instrument":0,"unverified":2,"pointer_only":0,"phrase":"5 ran (of which 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) · 2 unverified","official":{"repos":["thu-coai/bpo"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"evaluating-large-language-models-in","title":"Evaluating Large Language Models in Ophthalmology","date":"2023-11-07","arxiv_id":"2311.04933","n_code_links":0,"syntology":null},{"paper":null,"slug":"evaluating-multiple-large-language-models-in","title":"Evaluating multiple large language models in pediatric ophthalmology","date":"2023-11-07","arxiv_id":"2311.04368","n_code_links":0,"syntology":null},{"paper":null,"slug":"identifying-and-mitigating-vulnerabilities-in","title":"Identifying and Mitigating Vulnerabilities in LLM-Integrated Applications","date":"2023-11-07","arxiv_id":"2311.16153","n_code_links":0,"syntology":null},{"paper":null,"slug":"leveraging-large-language-models-for-3","title":"Leveraging Large Language Models for Automated Proof Synthesis in Rust","date":"2023-11-07","arxiv_id":"2311.03739","n_code_links":0,"syntology":null},{"paper":"/paper/multi-resolution-time-series-transformer-for","slug":"multi-resolution-time-series-transformer-for","title":"Multi-resolution Time-Series Transformer for Long-term Forecasting","date":"2023-11-07","arxiv_id":"2311.04147","n_code_links":2,"syntology":{"ran":2,"of":3,"n_ran_checked":2,"n_instrument":0,"unverified":1,"pointer_only":3,"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) · 1 unverified","official":{"repos":["networkslab/mtst","Yitiann/MTST"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"syntax-guided-transformers-elevating","title":"Syntax-Guided Transformers: Elevating Compositional Generalization and Grounding in Multimodal Environments","date":"2023-11-07","arxiv_id":"2311.04364","n_code_links":0,"syntology":null},{"paper":"/paper/towards-garment-sewing-pattern-reconstruction","slug":"towards-garment-sewing-pattern-reconstruction","title":"Towards Garment Sewing Pattern Reconstruction from a Single Image","date":"2023-11-07","arxiv_id":"2311.04218","n_code_links":2,"syntology":null},{"paper":null,"slug":"wearable-data-from-subjects-playing-super","title":"Wearable data from subjects playing Super Mario, sitting university exams, or performing physical exercise help detect acute mood episodes via self-supervised learning","date":"2023-11-07","arxiv_id":"2311.04215","n_code_links":0,"syntology":null},{"paper":"/paper/which-is-better-exploring-prompting-strategy","slug":"which-is-better-exploring-prompting-strategy","title":"Which is better? Exploring Prompting Strategy For LLM-based Metrics","date":"2023-11-07","arxiv_id":"2311.03754","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-simple-yet-efficient-ensemble-approach-for","title":"A Simple yet Efficient Ensemble Approach for AI-generated Text Detection","date":"2023-11-06","arxiv_id":"2311.03084","n_code_links":0,"syntology":null},{"paper":"/paper/can-llms-follow-simple-rules","slug":"can-llms-follow-simple-rules","title":"Can LLMs Follow Simple Rules?","date":"2023-11-06","arxiv_id":"2311.04235","n_code_links":1,"syntology":{"ran":1,"of":3,"n_ran_checked":1,"n_instrument":0,"unverified":2,"pointer_only":0,"phrase":"1 ran (of which 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) · 2 unverified","official":{"repos":["normster/llm_rules"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":"/paper/deepinception-hypnotize-large-language-model","slug":"deepinception-hypnotize-large-language-model","title":"DeepInception: Hypnotize Large Language Model to Be Jailbreaker","date":"2023-11-06","arxiv_id":"2311.03191","n_code_links":1,"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":["tmlr-group/deepinception"],"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":"leveraging-transformers-to-improve-breast","title":"Leveraging Transformers to Improve Breast Cancer Classification and Risk Assessment with Multi-modal and Longitudinal Data","date":"2023-11-06","arxiv_id":"2311.03217","n_code_links":0,"syntology":null},{"paper":null,"slug":"machine-learning-based-tea-leaf-disease","title":"Machine Learning-Based Tea Leaf Disease Detection: A Comprehensive Review","date":"2023-11-06","arxiv_id":"2311.03240","n_code_links":0,"syntology":null},{"paper":null,"slug":"nexus-at-araieval-shared-task-fine-tuning","title":"Nexus at ArAIEval Shared Task: Fine-Tuning Arabic Language Models for Propaganda and Disinformation Detection","date":"2023-11-06","arxiv_id":"2311.03184","n_code_links":0,"syntology":null},{"paper":null,"slug":"p-laplacian-transformer","title":"p-Laplacian Transformer","date":"2023-11-06","arxiv_id":"2311.03235","n_code_links":0,"syntology":null},{"paper":null,"slug":"scalable-and-transferable-black-box","title":"Scalable and Transferable Black-Box Jailbreaks for Language Models via Persona Modulation","date":"2023-11-06","arxiv_id":"2311.03348","n_code_links":0,"syntology":null},{"paper":null,"slug":"sugarvit-multi-objective-regression-of-uav","title":"SugarViT -- Multi-objective Regression of UAV Images with Vision Transformers and Deep Label Distribution Learning Demonstrated on Disease Severity Prediction in Sugar Beet","date":"2023-11-06","arxiv_id":"2311.03076","n_code_links":0,"syntology":null},{"paper":"/paper/tsp-transformer-task-specific-prompts-boosted","slug":"tsp-transformer-task-specific-prompts-boosted","title":"TSP-Transformer: Task-Specific Prompts Boosted Transformer for Holistic Scene Understanding","date":"2023-11-06","arxiv_id":"2311.03427","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-critical-perceptual-pre-trained-model-for","title":"A Critical Perceptual Pre-trained Model for Complex Trajectory Recovery","date":"2023-11-05","arxiv_id":"2311.02631","n_code_links":0,"syntology":null},{"paper":null,"slug":"evaluating-the-potential-of-leading-large","title":"Evaluating the Potential of Leading Large Language Models in Reasoning Biology Questions","date":"2023-11-05","arxiv_id":"2311.07582","n_code_links":0,"syntology":null},{"paper":"/paper/exploring-grounding-potential-of-vqa-oriented","slug":"exploring-grounding-potential-of-vqa-oriented","title":"GPT-4V-AD: Exploring Grounding Potential of VQA-oriented GPT-4V for Zero-shot Anomaly Detection","date":"2023-11-05","arxiv_id":"2311.02612","n_code_links":1,"syntology":null},{"paper":null,"slug":"floodbrain-flood-disaster-reporting-by-web","title":"FloodBrain: Flood Disaster Reporting by Web-based Retrieval Augmented Generation with an LLM","date":"2023-11-05","arxiv_id":"2311.02597","n_code_links":0,"syntology":null},{"paper":null,"slug":"large-language-models-implicitly-learn-to","title":"Large language models implicitly learn to straighten neural sentence trajectories to construct a predictive representation of natural language","date":"2023-11-05","arxiv_id":"2311.04930","n_code_links":0,"syntology":null},{"paper":"/paper/mftcoder-boosting-code-llms-with-multitask","slug":"mftcoder-boosting-code-llms-with-multitask","title":"MFTCoder: Boosting Code LLMs with Multitask Fine-Tuning","date":"2023-11-04","arxiv_id":"2311.02303","n_code_links":1,"syntology":null},{"paper":null,"slug":"ultra-long-sequence-distributed-transformer","title":"Ultra-Long Sequence Distributed Transformer","date":"2023-11-04","arxiv_id":"2311.02382","n_code_links":0,"syntology":null},{"paper":null,"slug":"understanding-the-natural-language-of-dna","title":"Understanding the Natural Language of DNA using Encoder-Decoder Foundation Models with Byte-level Precision","date":"2023-11-04","arxiv_id":"2311.02333","n_code_links":0,"syntology":null},{"paper":null,"slug":"an-empirical-study-of-benchmarking-chinese","title":"An Empirical Study of Benchmarking Chinese Aspect Sentiment Quad Prediction","date":"2023-11-03","arxiv_id":"2311.01713","n_code_links":0,"syntology":null},{"paper":null,"slug":"capturing-local-and-global-features-in","title":"Capturing Local and Global Features in Medical Images by Using Ensemble CNN-Transformer","date":"2023-11-03","arxiv_id":"2311.01731","n_code_links":0,"syntology":null},{"paper":null,"slug":"depth-guided-free-space-segmentation-for-a","title":"Depth-guided Free-space Segmentation for a Mobile Robot","date":"2023-11-03","arxiv_id":"2311.01966","n_code_links":0,"syntology":null},{"paper":"/paper/dialogbench-evaluating-llms-as-human-like","slug":"dialogbench-evaluating-llms-as-human-like","title":"DialogBench: Evaluating LLMs as Human-like Dialogue Systems","date":"2023-11-03","arxiv_id":"2311.01677","n_code_links":1,"syntology":null},{"paper":null,"slug":"emergence-of-abstract-state-representations","title":"Emergence of Abstract State Representations in Embodied Sequence Modeling","date":"2023-11-03","arxiv_id":"2311.02171","n_code_links":0,"syntology":null},{"paper":"/paper/gateloop-fully-data-controlled-linear","slug":"gateloop-fully-data-controlled-linear","title":"GateLoop: Fully Data-Controlled Linear Recurrence for Sequence Modeling","date":"2023-11-03","arxiv_id":"2311.01927","n_code_links":3,"syntology":{"ran":3,"of":5,"n_ran_checked":3,"n_instrument":0,"unverified":2,"pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 1 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","official":{"repos":["tobiaskatsch/GateLoop"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":2,"ran_from_kinds":["listed","official"]}}},{"paper":"/paper/multi-scale-time-stepping-of-partial","slug":"multi-scale-time-stepping-of-partial","title":"Multi-scale Time-stepping of Partial Differential Equations with Transformers","date":"2023-11-03","arxiv_id":"2311.02225","n_code_links":1,"syntology":null},{"paper":"/paper/pptc-benchmark-evaluating-large-language","slug":"pptc-benchmark-evaluating-large-language","title":"PPTC Benchmark: Evaluating Large Language Models for PowerPoint Task Completion","date":"2023-11-03","arxiv_id":"2311.01767","n_code_links":1,"syntology":{"ran":13,"of":17,"n_ran_checked":13,"n_instrument":0,"unverified":4,"pointer_only":0,"phrase":"13 ran (of which 0 constructed an object rather than computing a result; 13 with no instrument failure: 0 honoured, 0 violated, 13 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","official":{"repos":["gydpku/pptc"],"state":"official (archive's flag): 13 ran","n_ran":13,"n_constructed":0,"n_ran_no_instrument_failure":13,"n_unverified":4,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"the-potential-of-wearable-sensors-for","title":"The Potential of Wearable Sensors for Assessing Patient Acuity in Intensive Care Unit (ICU)","date":"2023-11-03","arxiv_id":"2311.02251","n_code_links":0,"syntology":null},{"paper":null,"slug":"the-risks-of-risk-based-ai-regulation-taking","title":"The risks of risk-based AI regulation: taking liability seriously","date":"2023-11-03","arxiv_id":"2311.14684","n_code_links":0,"syntology":null},{"paper":null,"slug":"towards-a-unified-transformer-based-framework","title":"Towards a Unified Transformer-based Framework for Scene Graph Generation and Human-object Interaction Detection","date":"2023-11-03","arxiv_id":"2311.01755","n_code_links":0,"syntology":null},{"paper":null,"slug":"atgnn-audio-tagging-graph-neural-network","title":"ATGNN: Audio Tagging Graph Neural Network","date":"2023-11-02","arxiv_id":"2311.01526","n_code_links":0,"syntology":null},{"paper":null,"slug":"deep-double-descent-for-time-series","title":"Deep Double Descent for Time Series Forecasting: Avoiding Undertrained Models","date":"2023-11-02","arxiv_id":"2311.01442","n_code_links":0,"syntology":null},{"paper":null,"slug":"distilling-knowledge-from-cnn-transformer","title":"Distilling Knowledge from CNN-Transformer Models for Enhanced Human Action Recognition","date":"2023-11-02","arxiv_id":"2311.01283","n_code_links":0,"syntology":null},{"paper":null,"slug":"efficient-vision-transformer-for-accurate","title":"Efficient Vision Transformer for Accurate Traffic Sign Detection","date":"2023-11-02","arxiv_id":"2311.01429","n_code_links":0,"syntology":null},{"paper":null,"slug":"enriching-phrases-with-coupled-pixel-and","title":"Enriching Phrases with Coupled Pixel and Object Contexts for Panoptic Narrative Grounding","date":"2023-11-02","arxiv_id":"2311.01091","n_code_links":0,"syntology":null},{"paper":null,"slug":"generative-input-towards-next-generation","title":"Generative Input: Towards Next-Generation Input Methods Paradigm","date":"2023-11-02","arxiv_id":"2311.01166","n_code_links":0,"syntology":null},{"paper":"/paper/hybrid-fusion-transformer-for-multisequence","slug":"hybrid-fusion-transformer-for-multisequence","title":"Hybrid-Fusion Transformer for Multisequence MRI","date":"2023-11-02","arxiv_id":"2311.01308","n_code_links":1,"syntology":null},{"paper":null,"slug":"learning-unsupervised-world-models-for","title":"Copilot4D: Learning Unsupervised World Models for Autonomous Driving via Discrete Diffusion","date":"2023-11-02","arxiv_id":"2311.01017","n_code_links":0,"syntology":null},{"paper":"/paper/m-m3d-multi-dataset-training-and-efficient","slug":"m-m3d-multi-dataset-training-and-efficient","title":"M&M3D: Multi-Dataset Training and Efficient Network for Multi-view 3D Object Detection","date":"2023-11-02","arxiv_id":"2311.00986","n_code_links":1,"syntology":null},{"paper":null,"slug":"on-the-convergence-of-encoder-only-shallow","title":"On the Convergence of Encoder-only Shallow Transformers","date":"2023-11-02","arxiv_id":"2311.01575","n_code_links":0,"syntology":null},{"paper":null,"slug":"scattering-vision-transformer-spectral-mixing","title":"Scattering Vision Transformer: Spectral Mixing Matters","date":"2023-11-02","arxiv_id":"2311.01310","n_code_links":0,"syntology":null},{"paper":"/paper/video2music-suitable-music-generation-from","slug":"video2music-suitable-music-generation-from","title":"Video2Music: Suitable Music Generation from Videos using an Affective Multimodal Transformer model","date":"2023-11-02","arxiv_id":"2311.00968","n_code_links":1,"syntology":{"ran":1,"of":5,"n_ran_checked":0,"n_instrument":1,"unverified":4,"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) · 4 unverified","official":{"repos":["amaai-lab/video2music"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":4,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"1dformer-learning-1d-landmark-representations","title":"1DFormer: a Transformer Architecture Learning 1D Landmark Representations for Facial Landmark Tracking","date":"2023-11-01","arxiv_id":"2311.00241","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-spatial-temporal-transformer-based","title":"A Spatial-Temporal Transformer based Framework For Human Pose Assessment And Correction in Education Scenarios","date":"2023-11-01","arxiv_id":"2311.00401","n_code_links":0,"syntology":null},{"paper":null,"slug":"are-large-language-models-reliable-judges-a","title":"Are Large Language Models Reliable Judges? A Study on the Factuality Evaluation Capabilities of LLMs","date":"2023-11-01","arxiv_id":"2311.00681","n_code_links":0,"syntology":null},{"paper":null,"slug":"attention-alignment-and-flexible-positional","title":"Attention Alignment and Flexible Positional Embeddings Improve Transformer Length Extrapolation","date":"2023-11-01","arxiv_id":"2311.00684","n_code_links":0,"syntology":null},{"paper":null,"slug":"can-large-language-models-capture-public","title":"Can Large Language Models Capture Public Opinion about Global Warming? An Empirical Assessment of Algorithmic Fidelity and Bias","date":"2023-11-01","arxiv_id":"2311.00217","n_code_links":0,"syntology":null},{"paper":"/paper/costar-improved-temporal-counterfactual","slug":"costar-improved-temporal-counterfactual","title":"COSTAR: Improved Temporal Counterfactual Estimation with Self-Supervised Learning","date":"2023-11-01","arxiv_id":"2311.00886","n_code_links":1,"syntology":null},{"paper":null,"slug":"detecting-visual-cues-in-the-intensive-care","title":"Detecting Visual Cues in the Intensive Care Unit and Association with Patient Clinical Status","date":"2023-11-01","arxiv_id":"2311.00565","n_code_links":0,"syntology":null},{"paper":"/paper/from-text-to-structure-using-large-language","slug":"from-text-to-structure-using-large-language","title":"From Text to Structure: Using Large Language Models to Support the Development of Legal Expert Systems","date":"2023-11-01","arxiv_id":"2311.04911","n_code_links":1,"syntology":null},{"paper":null,"slug":"improving-robustness-for-vision-transformer","title":"Improving Robustness for Vision Transformer with a Simple Dynamic Scanning Augmentation","date":"2023-11-01","arxiv_id":"2311.00441","n_code_links":0,"syntology":null},{"paper":null,"slug":"rethinking-decision-transformer-via","title":"Rethinking Decision Transformer via Hierarchical Reinforcement Learning","date":"2023-11-01","arxiv_id":"2311.00267","n_code_links":0,"syntology":null},{"paper":"/paper/the-development-of-llms-for-embodied","slug":"the-development-of-llms-for-embodied","title":"Advances in Embodied Navigation Using Large Language Models: A Survey","date":"2023-11-01","arxiv_id":"2311.00530","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-systematic-review-for-transformer-based","title":"A Systematic Review for Transformer-based Long-term Series Forecasting","date":"2023-10-31","arxiv_id":"2310.20218","n_code_links":0,"syntology":null},{"paper":null,"slug":"breathing-life-into-faces-speech-driven-3d","title":"Breathing Life into Faces: Speech-driven 3D Facial Animation with Natural Head Pose and Detailed Shape","date":"2023-10-31","arxiv_id":"2310.20240","n_code_links":0,"syntology":null},{"paper":"/paper/causal-interpretation-of-self-attention-in","slug":"causal-interpretation-of-self-attention-in","title":"Causal Interpretation of Self-Attention in Pre-Trained Transformers","date":"2023-10-31","arxiv_id":"2310.20307","n_code_links":1,"syntology":null},{"paper":null,"slug":"chipnemo-domain-adapted-llms-for-chip-design","title":"ChipNeMo: Domain-Adapted LLMs for Chip Design","date":"2023-10-31","arxiv_id":"2311.00176","n_code_links":0,"syntology":null},{"paper":null,"slug":"diversified-node-sampling-based-hierarchical","title":"Diversified Node Sampling based Hierarchical Transformer Pooling for Graph Representation Learning","date":"2023-10-31","arxiv_id":"2310.20250","n_code_links":0,"syntology":null},{"paper":null,"slug":"does-gpt-4-pass-the-turing-test","title":"Does GPT-4 pass the Turing test?","date":"2023-10-31","arxiv_id":"2310.20216","n_code_links":0,"syntology":null},{"paper":null,"slug":"efficient-classification-of-student-help","title":"Efficient Classification of Student Help Requests in Programming Courses Using Large Language Models","date":"2023-10-31","arxiv_id":"2310.20105","n_code_links":0,"syntology":null},{"paper":"/paper/generate-what-you-prefer-reshaping-sequential","slug":"generate-what-you-prefer-reshaping-sequential","title":"Generate What You Prefer: Reshaping Sequential Recommendation via Guided Diffusion","date":"2023-10-31","arxiv_id":"2310.20453","n_code_links":1,"syntology":null},{"paper":null,"slug":"global-transformer-architecture-for-indoor","title":"Global Transformer Architecture for Indoor Room Temperature Forecasting","date":"2023-10-31","arxiv_id":"2310.20476","n_code_links":0,"syntology":null},{"paper":null,"slug":"graphtransformers-for-geospatial-forecasting","title":"GraphTransformers for Geospatial Forecasting of Hurricane Trajectories","date":"2023-10-31","arxiv_id":"2310.20174","n_code_links":0,"syntology":null},{"paper":"/paper/in-search-of-lost-online-test-time-adaptation","slug":"in-search-of-lost-online-test-time-adaptation","title":"In Search of Lost Online Test-time Adaptation: A Survey","date":"2023-10-31","arxiv_id":"2310.20199","n_code_links":1,"syntology":{"ran":14,"of":17,"n_ran_checked":9,"n_instrument":5,"unverified":3,"pointer_only":7,"phrase":"14 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; 5 where Syntology's instrument failed) · 3 unverified","official":{"repos":["jo-wang/otta_vit_survey"],"state":"official (archive's flag): 14 ran","n_ran":14,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":3,"ran_from_kinds":["official"]}}},{"paper":"/paper/learning-from-mistakes-makes-llm-better","slug":"learning-from-mistakes-makes-llm-better","title":"Learning From Mistakes Makes LLM Better Reasoner","date":"2023-10-31","arxiv_id":"2310.20689","n_code_links":1,"syntology":{"ran":3,"of":4,"n_ran_checked":2,"n_instrument":1,"unverified":1,"pointer_only":0,"phrase":"3 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; 1 where Syntology's instrument failed) · 1 unverified","official":{"repos":["microsoft/lema"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"what-a-whole-slide-image-can-tell-subtype","title":"What a Whole Slide Image Can Tell? Subtype-guided Masked Transformer for Pathological Image Captioning","date":"2023-10-31","arxiv_id":"2310.20607","n_code_links":0,"syntology":null},{"paper":null,"slug":"bioinstruct-instruction-tuning-of-large","title":"BioInstruct: Instruction Tuning of Large Language Models for Biomedical Natural Language Processing","date":"2023-10-30","arxiv_id":"2310.19975","n_code_links":0,"syntology":null},{"paper":null,"slug":"building-real-world-meeting-summarization","title":"Building Real-World Meeting Summarization Systems using Large Language Models: A Practical Perspective","date":"2023-10-30","arxiv_id":"2310.19233","n_code_links":0,"syntology":null},{"paper":null,"slug":"constituency-parsing-using-llms","title":"Constituency Parsing using LLMs","date":"2023-10-30","arxiv_id":"2310.19462","n_code_links":0,"syntology":null},{"paper":"/paper/dynamics-of-instruction-tuning-each-ability","slug":"dynamics-of-instruction-tuning-each-ability","title":"Dynamics of Instruction Tuning: Each Ability of Large Language Models Has Its Own Growth Pace","date":"2023-10-30","arxiv_id":"2310.19651","n_code_links":1,"syntology":null}],"record_sha256":"e3785355e4e0d841d1a4fb8b1525ac72484ea0e366110d1f958960b456d970a2","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}