{"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/absolute-position-encodings/papers/63","list_of":"/method/absolute-position-encodings","method":"Absolute Position Encodings","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":63,"pages_in_order":140,"rows_per_page":100,"rows":[6201,6300],"of":13942,"counts":{"archive_papers_tagged":13942,"with_a_code_link":6505,"where_syntology_ran_a_sample":2224,"not_listed_spam_title":0,"listed":13942,"listed_where_code_ran":2224,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":1897,"every_run_a_failure_of_syntologys_instrument":327,"listed_with_a_run_with_no_instrument_failure":1897,"listed_every_run_a_failure_of_syntologys_instrument":327,"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/absolute-position-encodings","prev":"/method/absolute-position-encodings/papers/62","next":"/method/absolute-position-encodings/papers/64","papers":[{"paper":null,"slug":"sara-rt-scaling-up-robotics-transformers-with","title":"SARA-RT: Scaling up Robotics Transformers with Self-Adaptive Robust Attention","date":"2023-12-04","arxiv_id":"2312.01990","n_code_links":0,"syntology":null},{"paper":"/paper/sequencepar-understanding-pedestrian","slug":"sequencepar-understanding-pedestrian","title":"SequencePAR: Understanding Pedestrian Attributes via A Sequence Generation Paradigm","date":"2023-12-04","arxiv_id":"2312.01640","n_code_links":2,"syntology":null},{"paper":"/paper/tree-of-attacks-jailbreaking-black-box-llms","slug":"tree-of-attacks-jailbreaking-black-box-llms","title":"Tree of Attacks: Jailbreaking Black-Box LLMs Automatically","date":"2023-12-04","arxiv_id":"2312.02119","n_code_links":2,"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":["ricommunity/tap"],"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":"vaquita-enhancing-alignment-in-llm-assisted","title":"VaQuitA: Enhancing Alignment in LLM-Assisted Video Understanding","date":"2023-12-04","arxiv_id":"2312.02310","n_code_links":0,"syntology":null},{"paper":"/paper/automatic-report-generation-for-1","slug":"automatic-report-generation-for-1","title":"Automatic Report Generation for Histopathology images using pre-trained Vision Transformers and BERT","date":"2023-12-03","arxiv_id":"2312.01435","n_code_links":1,"syntology":null},{"paper":"/paper/d-bot-database-diagnosis-system-using-large","slug":"d-bot-database-diagnosis-system-using-large","title":"D-Bot: Database Diagnosis System using Large Language Models","date":"2023-12-03","arxiv_id":"2312.01454","n_code_links":1,"syntology":{"ran":1,"of":3,"n_ran_checked":1,"n_instrument":0,"unverified":2,"pointer_only":3,"phrase":"1 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; 0 where Syntology's instrument failed) · 2 unverified","official":{"repos":["tsinghuadatabasegroup/db-gpt"],"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":null,"slug":"estformer-transformer-utilizing","title":"ESTformer: Transformer Utilizing Spatiotemporal Dependencies for Electroencaphalogram Super-resolution","date":"2023-12-03","arxiv_id":"2312.10052","n_code_links":0,"syntology":null},{"paper":null,"slug":"mabvit-modified-attention-block-enhances","title":"MABViT -- Modified Attention Block Enhances Vision Transformers","date":"2023-12-03","arxiv_id":"2312.01324","n_code_links":0,"syntology":null},{"paper":null,"slug":"transformers-are-uninterpretable-with-myopic-1","title":"Transformers are uninterpretable with myopic methods: a case study with bounded Dyck grammars","date":"2023-12-03","arxiv_id":"2312.01429","n_code_links":0,"syntology":null},{"paper":null,"slug":"axiomatic-preference-modeling-for-longform","title":"Axiomatic Preference Modeling for Longform Question Answering","date":"2023-12-02","arxiv_id":"2312.02206","n_code_links":0,"syntology":null},{"paper":"/paper/bootstrapping-interactive-image-text","slug":"bootstrapping-interactive-image-text","title":"Bootstrapping Interactive Image-Text Alignment for Remote Sensing Image Captioning","date":"2023-12-02","arxiv_id":"2312.01191","n_code_links":1,"syntology":null},{"paper":null,"slug":"english-to-arabic-machine-translation-of","title":"English to Arabic machine translation of mathematical documents","date":"2023-12-02","arxiv_id":"2312.03753","n_code_links":0,"syntology":null},{"paper":null,"slug":"from-voices-to-validity-leveraging-large","title":"From Voices to Validity: Leveraging Large Language Models (LLMs) for Textual Analysis of Policy Stakeholder Interviews","date":"2023-12-02","arxiv_id":"2312.01202","n_code_links":0,"syntology":null},{"paper":"/paper/idpl-pfod2-a-new-large-scale-dataset-for","slug":"idpl-pfod2-a-new-large-scale-dataset-for","title":"IDPL-PFOD2: A New Large-Scale Dataset for Printed Farsi Optical Character Recognition","date":"2023-12-02","arxiv_id":"2312.01177","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-bayesian-approach-for-prompt-optimization","title":"A Bayesian approach for prompt optimization in pre-trained language models","date":"2023-12-01","arxiv_id":"2312.00471","n_code_links":0,"syntology":null},{"paper":"/paper/bcn-batch-channel-normalization-for-image","slug":"bcn-batch-channel-normalization-for-image","title":"BCN: Batch Channel Normalization for Image Classification","date":"2023-12-01","arxiv_id":"2312.00596","n_code_links":1,"syntology":null},{"paper":"/paper/deep-unlearning-fast-and-efficient-training","slug":"deep-unlearning-fast-and-efficient-training","title":"Deep Unlearning: Fast and Efficient Gradient-free Approach to Class Forgetting","date":"2023-12-01","arxiv_id":"2312.00761","n_code_links":1,"syntology":{"ran":10,"of":17,"n_ran_checked":10,"n_instrument":0,"unverified":7,"pointer_only":17,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 0 honoured, 0 violated, 10 with no contract checked; 0 where Syntology's instrument failed) · 7 unverified","official":{"repos":["sangamesh-kodge/class_forgetting"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":7,"ran_from_kinds":["official"]}}},{"paper":"/paper/efficientsam-leveraged-masked-image","slug":"efficientsam-leveraged-masked-image","title":"EfficientSAM: Leveraged Masked Image Pretraining for Efficient Segment Anything","date":"2023-12-01","arxiv_id":"2312.00863","n_code_links":1,"syntology":null},{"paper":null,"slug":"event-recognition-in-laparoscopic-gynecology","title":"Event Recognition in Laparoscopic Gynecology Videos with Hybrid Transformers","date":"2023-12-01","arxiv_id":"2312.00593","n_code_links":0,"syntology":null},{"paper":"/paper/gift-generative-interpretable-fine-tuning","slug":"gift-generative-interpretable-fine-tuning","title":"Generative Parameter-Efficient Fine-Tuning","date":"2023-12-01","arxiv_id":"2312.00700","n_code_links":1,"syntology":{"ran":12,"of":16,"n_ran_checked":12,"n_instrument":0,"unverified":4,"pointer_only":10,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 12 with no instrument failure: 0 honoured, 0 violated, 12 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","official":{"repos":["savadikarc/gift"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":12,"n_unverified":4,"ran_from_kinds":["official"]}}},{"paper":"/paper/learning-to-estimate-critical-gait-parameters","slug":"learning-to-estimate-critical-gait-parameters","title":"Learning to Estimate Critical Gait Parameters from Single-View RGB Videos with Transformer-Based Attention Network","date":"2023-12-01","arxiv_id":"2312.00398","n_code_links":1,"syntology":null},{"paper":"/paper/mamba-linear-time-sequence-modeling-with","slug":"mamba-linear-time-sequence-modeling-with","title":"Mamba: Linear-Time Sequence Modeling with Selective State Spaces","date":"2023-12-01","arxiv_id":"2312.00752","n_code_links":35,"syntology":{"ran":28,"of":62,"n_ran_checked":21,"n_instrument":7,"unverified":34,"pointer_only":29,"phrase":"28 ran (of which 7 constructed an object rather than computing a result; 21 with no instrument failure: 0 honoured, 0 violated, 21 with no contract checked; 7 where Syntology's instrument failed) · 34 unverified","official":{"repos":["state-spaces/mamba","radarFudan/mamba"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":null,"slug":"mitigating-over-smoothing-in-transformers-via-1","title":"Mitigating Over-smoothing in Transformers via Regularized Nonlocal Functionals","date":"2023-12-01","arxiv_id":"2312.00751","n_code_links":0,"syntology":null},{"paper":null,"slug":"nonparametric-variational-regularisation-of","title":"Nonparametric Variational Regularisation of Pretrained Transformers","date":"2023-12-01","arxiv_id":"2312.00662","n_code_links":0,"syntology":null},{"paper":"/paper/quick-back-translation-for-unsupervised","slug":"quick-back-translation-for-unsupervised","title":"Quick Back-Translation for Unsupervised Machine Translation","date":"2023-12-01","arxiv_id":"2312.00912","n_code_links":1,"syntology":null},{"paper":null,"slug":"spatiotemporal-transformer-for-imputing","title":"Spatiotemporal Transformer for Imputing Sparse Data: A Deep Learning Approach","date":"2023-12-01","arxiv_id":"2312.00963","n_code_links":0,"syntology":null},{"paper":"/paper/synfundus-generating-a-synthetic-fundus","slug":"synfundus-generating-a-synthetic-fundus","title":"SynFundus-1M: A High-quality Million-scale Synthetic fundus images Dataset with Fifteen Types of Annotation","date":"2023-12-01","arxiv_id":"2312.00377","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-lightweight-clustering-framework-for","title":"A Lightweight Clustering Framework for Unsupervised Semantic Segmentation","date":"2023-11-30","arxiv_id":"2311.18628","n_code_links":0,"syntology":null},{"paper":null,"slug":"applying-large-language-models-and-chain-of","title":"Applying Large Language Models and Chain-of-Thought for Automatic Scoring","date":"2023-11-30","arxiv_id":"2312.03748","n_code_links":0,"syntology":null},{"paper":"/paper/bam-detr-boundary-aligned-moment-detection","slug":"bam-detr-boundary-aligned-moment-detection","title":"BAM-DETR: Boundary-Aligned Moment Detection Transformer for Temporal Sentence Grounding in Videos","date":"2023-11-30","arxiv_id":"2312.00083","n_code_links":1,"syntology":{"ran":9,"of":11,"n_ran_checked":8,"n_instrument":1,"unverified":2,"pointer_only":11,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 1 honoured, 0 violated, 7 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","official":{"repos":["Pilhyeon/BAM-DETR"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"brainformer-modeling-mri-brain-functions-to","title":"Brainformer: Mimic Human Visual Brain Functions to Machine Vision Models via fMRI","date":"2023-11-30","arxiv_id":"2312.00236","n_code_links":0,"syntology":null},{"paper":null,"slug":"categorical-traffic-transformer-interpretable","title":"Categorical Traffic Transformer: Interpretable and Diverse Behavior Prediction with Tokenized Latent","date":"2023-11-30","arxiv_id":"2311.18307","n_code_links":0,"syntology":null},{"paper":"/paper/critiquellm-scaling-llm-as-critic-for","slug":"critiquellm-scaling-llm-as-critic-for","title":"CritiqueLLM: Towards an Informative Critique Generation Model for Evaluation of Large Language Model Generation","date":"2023-11-30","arxiv_id":"2311.18702","n_code_links":2,"syntology":{"ran":4,"of":5,"n_ran_checked":4,"n_instrument":0,"unverified":1,"pointer_only":5,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 1 honoured, 3 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","official":{"repos":["thu-coai/critiquellm"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"diffusion-models-without-attention","title":"Diffusion Models Without Attention","date":"2023-11-30","arxiv_id":"2311.18257","n_code_links":0,"syntology":null},{"paper":null,"slug":"hot-higher-order-dynamic-graph-representation","title":"HOT: Higher-Order Dynamic Graph Representation Learning with Efficient Transformers","date":"2023-11-30","arxiv_id":"2311.18526","n_code_links":0,"syntology":null},{"paper":null,"slug":"multiresformer-transformer-with-adaptive","title":"MultiResFormer: Transformer with Adaptive Multi-Resolution Modeling for General Time Series Forecasting","date":"2023-11-30","arxiv_id":"2311.18780","n_code_links":0,"syntology":null},{"paper":null,"slug":"omnimotiongpt-animal-motion-generation-with","title":"OmniMotionGPT: Animal Motion Generation with Limited Data","date":"2023-11-30","arxiv_id":"2311.18303","n_code_links":0,"syntology":null},{"paper":null,"slug":"relevance-guided-neural-machine-translation","title":"Relevance-guided Neural Machine Translation","date":"2023-11-30","arxiv_id":"2312.00214","n_code_links":0,"syntology":null},{"paper":"/paper/semantic-aware-frame-event-fusion-based","slug":"semantic-aware-frame-event-fusion-based","title":"Semantic-Aware Frame-Event Fusion based Pattern Recognition via Large Vision-Language Models","date":"2023-11-30","arxiv_id":"2311.18592","n_code_links":1,"syntology":null},{"paper":"/paper/solving-the-team-orienteering-problem-with-1","slug":"solving-the-team-orienteering-problem-with-1","title":"TOP-Former: A Multi-Agent Transformer Approach for the Team Orienteering Problem","date":"2023-11-30","arxiv_id":"2311.18662","n_code_links":1,"syntology":null},{"paper":"/paper/unnatural-error-correction-gpt-4-can-almost","slug":"unnatural-error-correction-gpt-4-can-almost","title":"Unnatural Error Correction: GPT-4 Can Almost Perfectly Handle Unnatural Scrambled Text","date":"2023-11-30","arxiv_id":"2311.18805","n_code_links":1,"syntology":{"ran":3,"of":6,"n_ran_checked":3,"n_instrument":0,"unverified":3,"pointer_only":6,"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) · 3 unverified","official":{"repos":["ccqq77/unnatural-error-correction"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":3,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"aviationgpt-a-large-language-model-for-the","title":"AviationGPT: A Large Language Model for the Aviation Domain","date":"2023-11-29","arxiv_id":"2311.17686","n_code_links":0,"syntology":null},{"paper":"/paper/betrayed-by-attention-a-simple-yet-effective","slug":"betrayed-by-attention-a-simple-yet-effective","title":"Betrayed by Attention: A Simple yet Effective Approach for Self-supervised Video Object Segmentation","date":"2023-11-29","arxiv_id":"2311.17893","n_code_links":1,"syntology":{"ran":10,"of":12,"n_ran_checked":8,"n_instrument":2,"unverified":2,"pointer_only":12,"phrase":"10 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; 2 where Syntology's instrument failed) · 2 unverified","official":{"repos":["shvdiwnkozbw/ssl-uvos"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":"/paper/biomedical-knowledge-graph-enhanced-prompt","slug":"biomedical-knowledge-graph-enhanced-prompt","title":"Biomedical knowledge graph-optimized prompt generation for large language models","date":"2023-11-29","arxiv_id":"2311.17330","n_code_links":1,"syntology":{"ran":0,"of":3,"n_ran_checked":0,"n_instrument":0,"unverified":3,"pointer_only":0,"phrase":"0 ran · 3 unverified","official":{"repos":["BaranziniLab/KG_RAG"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":3,"ran_from_kinds":[]}}},{"paper":"/paper/chatillusion-efficient-aligning-interleaved","slug":"chatillusion-efficient-aligning-interleaved","title":"M$^{2}$Chat: Empowering VLM for Multimodal LLM Interleaved Text-Image Generation","date":"2023-11-29","arxiv_id":"2311.17963","n_code_links":1,"syntology":null},{"paper":"/paper/cross-scope-spatial-spectral-information","slug":"cross-scope-spatial-spectral-information","title":"Cross-Scope Spatial-Spectral Information Aggregation for Hyperspectral Image Super-Resolution","date":"2023-11-29","arxiv_id":"2311.17340","n_code_links":1,"syntology":null},{"paper":"/paper/focus-on-query-adversarial-mining-transformer-1","slug":"focus-on-query-adversarial-mining-transformer-1","title":"Focus on Query: Adversarial Mining Transformer for Few-Shot Segmentation","date":"2023-11-29","arxiv_id":"2311.17626","n_code_links":1,"syntology":{"ran":13,"of":14,"n_ran_checked":6,"n_instrument":7,"unverified":1,"pointer_only":14,"phrase":"13 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 7 where Syntology's instrument failed) · 1 unverified","official":{"repos":["wyxdm/amnet"],"state":"official (archive's flag): 13 ran","n_ran":13,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"generative-hierarchical-temporal-transformer","title":"Generative Hierarchical Temporal Transformer for Hand Pose and Action Modeling","date":"2023-11-29","arxiv_id":"2311.17366","n_code_links":0,"syntology":null},{"paper":null,"slug":"grounding-foundation-models-through-federated","title":"Grounding Foundation Models through Federated Transfer Learning: A General Framework","date":"2023-11-29","arxiv_id":"2311.17431","n_code_links":0,"syntology":null},{"paper":"/paper/introduction-to-transformers-an-nlp","slug":"introduction-to-transformers-an-nlp","title":"Introduction to Transformers: an NLP Perspective","date":"2023-11-29","arxiv_id":"2311.17633","n_code_links":1,"syntology":null},{"paper":null,"slug":"layercollapse-adaptive-compression-of-neural","title":"LayerCollapse: Adaptive compression of neural networks","date":"2023-11-29","arxiv_id":"2311.17943","n_code_links":0,"syntology":null},{"paper":null,"slug":"mm-narrator-narrating-long-form-videos-with","title":"MM-Narrator: Narrating Long-form Videos with Multimodal In-Context Learning","date":"2023-11-29","arxiv_id":"2311.17435","n_code_links":0,"syntology":null},{"paper":"/paper/momask-generative-masked-modeling-of-3d-human","slug":"momask-generative-masked-modeling-of-3d-human","title":"MoMask: Generative Masked Modeling of 3D Human Motions","date":"2023-11-29","arxiv_id":"2312.00063","n_code_links":1,"syntology":{"ran":9,"of":11,"n_ran_checked":8,"n_instrument":1,"unverified":2,"pointer_only":5,"phrase":"9 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; 1 where Syntology's instrument failed) · 2 unverified","official":{"repos":["EricGuo5513/momask-codes"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":"/paper/pose-anything-a-graph-based-approach-for","slug":"pose-anything-a-graph-based-approach-for","title":"A Graph-Based Approach for Category-Agnostic Pose Estimation","date":"2023-11-29","arxiv_id":"2311.17891","n_code_links":2,"syntology":{"ran":2,"of":2,"n_ran_checked":0,"n_instrument":2,"unverified":0,"pointer_only":2,"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":["orhir/PoseAnything"],"state":"official: no sample here; runs from other or unrecorded repositories","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["unlocated"]}}},{"paper":"/paper/pvit-6d-overclocking-vision-transformers-for","slug":"pvit-6d-overclocking-vision-transformers-for","title":"PViT-6D: Overclocking Vision Transformers for 6D Pose Estimation with Confidence-Level Prediction and Pose Tokens","date":"2023-11-29","arxiv_id":"2311.17504","n_code_links":1,"syntology":null},{"paper":null,"slug":"race-it-a-reconfigurable-analog-cam-crossbar","title":"RACE-IT: A Reconfigurable Analog CAM-Crossbar Engine for In-Memory Transformer Acceleration","date":"2023-11-29","arxiv_id":"2312.06532","n_code_links":0,"syntology":null},{"paper":"/paper/sigformer-sparse-signal-guided-transformer","slug":"sigformer-sparse-signal-guided-transformer","title":"SigFormer: Sparse Signal-Guided Transformer for Multi-Modal Human Action Segmentation","date":"2023-11-29","arxiv_id":"2311.17428","n_code_links":1,"syntology":null},{"paper":"/paper/timebench-a-comprehensive-evaluation-of","slug":"timebench-a-comprehensive-evaluation-of","title":"TimeBench: A Comprehensive Evaluation of Temporal Reasoning Abilities in Large Language Models","date":"2023-11-29","arxiv_id":"2311.17667","n_code_links":1,"syntology":null},{"paper":null,"slug":"timelygpt-recurrent-convolutional-transformer","title":"TimelyGPT: Extrapolatable Transformer Pre-training for Long-term Time-Series Forecasting in Healthcare","date":"2023-11-29","arxiv_id":"2312.00817","n_code_links":0,"syntology":null},{"paper":null,"slug":"wireless-network-digital-twin-for-6g","title":"Wireless Network Digital Twin for 6G: Generative AI as A Key Enabler","date":"2023-11-29","arxiv_id":"2311.17451","n_code_links":0,"syntology":null},{"paper":"/paper/can-generalist-foundation-models-outcompete","slug":"can-generalist-foundation-models-outcompete","title":"Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicine","date":"2023-11-28","arxiv_id":"2311.16452","n_code_links":2,"syntology":{"ran":9,"of":10,"n_ran_checked":9,"n_instrument":0,"unverified":1,"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) · 1 unverified","official":null}},{"paper":null,"slug":"cole-a-hierarchical-generation-framework-for","title":"COLE: A Hierarchical Generation Framework for Multi-Layered and Editable Graphic Design","date":"2023-11-28","arxiv_id":"2311.16974","n_code_links":0,"syntology":null},{"paper":null,"slug":"comparing-generative-chatbots-based-on","title":"Comparing Generative Chatbots Based on Process Requirements","date":"2023-11-28","arxiv_id":"2312.03741","n_code_links":0,"syntology":null},{"paper":null,"slug":"contextseg-sketch-semantic-segmentation-by","title":"ContextSeg: Sketch Semantic Segmentation by Querying the Context with Attention","date":"2023-11-28","arxiv_id":"2311.16682","n_code_links":0,"syntology":null},{"paper":"/paper/deu-net-dual-encoder-u-net-for-automated-skin","slug":"deu-net-dual-encoder-u-net-for-automated-skin","title":"DEU-Net: Dual-Encoder U-Net for Automated Skin Lesion Segmentation","date":"2023-11-28","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"general-purpose-vs-domain-adapted-large","title":"General-Purpose vs. Domain-Adapted Large Language Models for Extraction of Structured Data from Chest Radiology Reports","date":"2023-11-28","arxiv_id":"2311.17213","n_code_links":0,"syntology":null},{"paper":"/paper/phg-net-persistent-homology-guided-medical","slug":"phg-net-persistent-homology-guided-medical","title":"PHG-Net: Persistent Homology Guided Medical Image Classification","date":"2023-11-28","arxiv_id":"2311.17243","n_code_links":1,"syntology":{"ran":4,"of":9,"n_ran_checked":4,"n_instrument":0,"unverified":5,"pointer_only":9,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 2 honoured, 1 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 5 unverified","official":{"repos":["yaoppeng/topoclassification"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":5,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"scaling-political-texts-with-chatgpt","title":"Positioning Political Texts with Large Language Models by Asking and Averaging","date":"2023-11-28","arxiv_id":"2311.16639","n_code_links":0,"syntology":null},{"paper":"/paper/self-training-solutions-for-the-iccv-2023","slug":"self-training-solutions-for-the-iccv-2023","title":"Self-training solutions for the ICCV 2023 GeoNet Challenge","date":"2023-11-28","arxiv_id":"2311.16843","n_code_links":1,"syntology":null},{"paper":null,"slug":"str-cert-robustness-certification-for-deep","title":"STR-Cert: Robustness Certification for Deep Text Recognition on Deep Learning Pipelines and Vision Transformers","date":"2023-11-28","arxiv_id":"2401.05338","n_code_links":0,"syntology":null},{"paper":"/paper/the-falcon-series-of-open-language-models","slug":"the-falcon-series-of-open-language-models","title":"The Falcon Series of Open Language Models","date":"2023-11-28","arxiv_id":"2311.16867","n_code_links":0,"syntology":null},{"paper":null,"slug":"tlcontrol-trajectory-and-language-control-for","title":"TLControl: Trajectory and Language Control for Human Motion Synthesis","date":"2023-11-28","arxiv_id":"2311.17135","n_code_links":0,"syntology":null},{"paper":null,"slug":"aligning-non-causal-factors-for-transformer","title":"Aligning Non-Causal Factors for Transformer-Based Source-Free Domain Adaptation","date":"2023-11-27","arxiv_id":"2311.16294","n_code_links":0,"syntology":null},{"paper":"/paper/can-vision-language-models-think-from-a-first","slug":"can-vision-language-models-think-from-a-first","title":"EgoThink: Evaluating First-Person Perspective Thinking Capability of Vision-Language Models","date":"2023-11-27","arxiv_id":"2311.15596","n_code_links":1,"syntology":null},{"paper":null,"slug":"chartllama-a-multimodal-llm-for-chart","title":"ChartLlama: A Multimodal LLM for Chart Understanding and Generation","date":"2023-11-27","arxiv_id":"2311.16483","n_code_links":0,"syntology":null},{"paper":null,"slug":"decoding-logic-errors-a-comparative-study-on","title":"Decoding Logic Errors: A Comparative Study on Bug Detection by Students and Large Language Models","date":"2023-11-27","arxiv_id":"2311.16017","n_code_links":0,"syntology":null},{"paper":null,"slug":"eafp-med-an-efficient-adaptive-feature","title":"EAFP-Med: An Efficient Adaptive Feature Processing Module Based on Prompts for Medical Image Detection","date":"2023-11-27","arxiv_id":"2311.15540","n_code_links":0,"syntology":null},{"paper":"/paper/efficient-pre-training-for-localized","slug":"efficient-pre-training-for-localized","title":"Efficient Pre-training for Localized Instruction Generation of Videos","date":"2023-11-27","arxiv_id":"2311.15964","n_code_links":1,"syntology":null},{"paper":"/paper/gpt4vis-what-can-gpt-4-do-for-zero-shot","slug":"gpt4vis-what-can-gpt-4-do-for-zero-shot","title":"GPT4Vis: What Can GPT-4 Do for Zero-shot Visual Recognition?","date":"2023-11-27","arxiv_id":"2311.15732","n_code_links":2,"syntology":{"ran":2,"of":2,"n_ran_checked":1,"n_instrument":1,"unverified":0,"pointer_only":1,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["whwu95/GPT4Vis"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"instruct2attack-language-guided-semantic","title":"Instruct2Attack: Language-Guided Semantic Adversarial Attacks","date":"2023-11-27","arxiv_id":"2311.15551","n_code_links":0,"syntology":null},{"paper":null,"slug":"machine-learning-based-jamun-leaf-disease","title":"Machine Learning-Based Jamun Leaf Disease Detection: A Comprehensive Review","date":"2023-11-27","arxiv_id":"2311.15741","n_code_links":0,"syntology":null},{"paper":"/paper/ssin-self-supervised-learning-for-rainfall","slug":"ssin-self-supervised-learning-for-rainfall","title":"SSIN: Self-Supervised Learning for Rainfall Spatial Interpolation","date":"2023-11-27","arxiv_id":"2311.15530","n_code_links":1,"syntology":null},{"paper":null,"slug":"technical-report-for-argoverse-challenges-on","title":"Technical Report for Argoverse Challenges on 4D Occupancy Forecasting","date":"2023-11-27","arxiv_id":"2311.15660","n_code_links":0,"syntology":null},{"paper":null,"slug":"towards-vision-enhancing-llms-empowering","title":"Towards Vision Enhancing LLMs: Empowering Multimodal Knowledge Storage and Sharing in LLMs","date":"2023-11-27","arxiv_id":"2311.15759","n_code_links":0,"syntology":null},{"paper":null,"slug":"assessing-ai-chatbots-performance-in","title":"Comparative Analysis of ChatGPT, GPT-4, and Microsoft Bing Chatbots for GRE Test","date":"2023-11-26","arxiv_id":"2312.03719","n_code_links":0,"syntology":null},{"paper":"/paper/chada-vit-channel-adaptive-attention-for","slug":"chada-vit-channel-adaptive-attention-for","title":"ChAda-ViT : Channel Adaptive Attention for Joint Representation Learning of Heterogeneous Microscopy Images","date":"2023-11-26","arxiv_id":"2311.15264","n_code_links":2,"syntology":{"ran":6,"of":6,"n_ran_checked":6,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["nicoboou/chadavit","nicoboou/chada_vit"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"spectro-vit-a-vision-transformer-model-for","title":"Spectro-ViT: A Vision Transformer Model for GABA-edited MRS Reconstruction Using Spectrograms","date":"2023-11-26","arxiv_id":"2311.15386","n_code_links":0,"syntology":null},{"paper":null,"slug":"ultra-range-gesture-recognition-using-an-rgb","title":"Ultra-Range Gesture Recognition using a Web-Camera in Human-Robot Interaction","date":"2023-11-26","arxiv_id":"2311.15361","n_code_links":0,"syntology":null},{"paper":"/paper/autoeval-video-an-automatic-benchmark-for","slug":"autoeval-video-an-automatic-benchmark-for","title":"AutoEval-Video: An Automatic Benchmark for Assessing Large Vision Language Models in Open-Ended Video Question Answering","date":"2023-11-25","arxiv_id":"2311.14906","n_code_links":1,"syntology":null},{"paper":null,"slug":"from-text-to-image-exploring-gpt-4vision-s","title":"From Text to Image: Exploring GPT-4Vision's Potential in Advanced Radiological Analysis across Subspecialties","date":"2023-11-24","arxiv_id":"2311.14777","n_code_links":0,"syntology":null},{"paper":"/paper/llamol-a-dynamic-multi-conditional-generative","slug":"llamol-a-dynamic-multi-conditional-generative","title":"LLamol: A Dynamic Multi-Conditional Generative Transformer for De Novo Molecular Design","date":"2023-11-24","arxiv_id":"2311.14407","n_code_links":1,"syntology":null},{"paper":"/paper/one-fits-all-universal-time-series-analysis","slug":"one-fits-all-universal-time-series-analysis","title":"Understanding the Role of Textual Prompts in LLM for Time Series Forecasting: an Adapter View","date":"2023-11-24","arxiv_id":"2311.14782","n_code_links":1,"syntology":null},{"paper":null,"slug":"rsb-pose-robust-short-baseline-binocular-3d","title":"RSB-Pose: Robust Short-Baseline Binocular 3D Human Pose Estimation with Occlusion Handling","date":"2023-11-24","arxiv_id":"2311.14242","n_code_links":0,"syntology":null},{"paper":null,"slug":"tvt-training-free-vision-transformer-search","title":"TVT: Training-Free Vision Transformer Search on Tiny Datasets","date":"2023-11-24","arxiv_id":"2311.14337","n_code_links":0,"syntology":null},{"paper":null,"slug":"auditing-and-mitigating-cultural-bias-in-llms","title":"Cultural Bias and Cultural Alignment of Large Language Models","date":"2023-11-23","arxiv_id":"2311.14096","n_code_links":0,"syntology":null},{"paper":"/paper/evaluating-gpt-4-s-vision-capabilities-on","slug":"evaluating-gpt-4-s-vision-capabilities-on","title":"Evaluating GPT-4's Vision Capabilities on Brazilian University Admission Exams","date":"2023-11-23","arxiv_id":"2311.14169","n_code_links":1,"syntology":null},{"paper":null,"slug":"fvit-grasp-grasping-objects-with-using-fast","title":"FViT-Grasp: Grasping Objects With Using Fast Vision Transformers","date":"2023-11-23","arxiv_id":"2311.13986","n_code_links":0,"syntology":null},{"paper":null,"slug":"knowledge-distillation-based-semantic","title":"Knowledge Distillation Based Semantic Communications For Multiple Users","date":"2023-11-23","arxiv_id":"2311.13789","n_code_links":0,"syntology":null},{"paper":"/paper/lacformer-toward-accurate-and-efficient-polyp","slug":"lacformer-toward-accurate-and-efficient-polyp","title":"LACFormer: Toward accurate and efficient polyp segmentation","date":"2023-11-23","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"progressive-learning-with-visual-prompt","title":"Progressive Learning with Visual Prompt Tuning for Variable-Rate Image Compression","date":"2023-11-23","arxiv_id":"2311.13846","n_code_links":0,"syntology":null}],"record_sha256":"a6ad6916ed2974a31c4fd976c82c6fa5cf5feb8f4bdc06f0ede5f30d675788b4","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}