{"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/linear-layer/papers/58","list_of":"/method/linear-layer","method":"Linear Layer","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":58,"pages_in_order":255,"rows_per_page":100,"rows":[5701,5800],"of":25421,"counts":{"archive_papers_tagged":25421,"with_a_code_link":11479,"where_syntology_ran_a_sample":3523,"not_listed_spam_title":0,"listed":25421,"listed_where_code_ran":3523,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":2976,"every_run_a_failure_of_syntologys_instrument":547,"listed_with_a_run_with_no_instrument_failure":2976,"listed_every_run_a_failure_of_syntologys_instrument":547,"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/linear-layer","prev":"/method/linear-layer/papers/57","next":"/method/linear-layer/papers/59","papers":[{"paper":"/paper/adaptive-foundation-models-for-online","slug":"adaptive-foundation-models-for-online","title":"Scalable Exploration via Ensemble++","date":"2024-07-18","arxiv_id":"2407.13195","n_code_links":2,"syntology":{"ran":5,"of":8,"n_ran_checked":3,"n_instrument":2,"unverified":3,"pointer_only":8,"phrase":"5 ran (of which 1 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 2 where Syntology's instrument failed) · 3 unverified","official":{"repos":["szrlee/GPT-HyperAgent","szrlee/ensemble_plus_plus"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":1,"n_ran_no_instrument_failure":3,"n_unverified":3,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"black-box-opinion-manipulation-attacks-to","title":"Black-Box Opinion Manipulation Attacks to Retrieval-Augmented Generation of Large Language Models","date":"2024-07-18","arxiv_id":"2407.13757","n_code_links":0,"syntology":null},{"paper":"/paper/can-open-source-llms-compete-with-commercial","slug":"can-open-source-llms-compete-with-commercial","title":"Can Open-Source LLMs Compete with Commercial Models? Exploring the Few-Shot Performance of Current GPT Models in Biomedical Tasks","date":"2024-07-18","arxiv_id":"2407.13511","n_code_links":1,"syntology":null},{"paper":null,"slug":"continual-distillation-learning","title":"Continual Distillation Learning: Knowledge Distillation in Prompt-based Continual Learning","date":"2024-07-18","arxiv_id":"2407.13911","n_code_links":0,"syntology":null},{"paper":null,"slug":"dream-a-biomedical-data-driven-self-evolving","title":"Autonomous self-evolving research on biomedical data: the DREAM paradigm","date":"2024-07-18","arxiv_id":"2407.13637","n_code_links":0,"syntology":null},{"paper":"/paper/evaluating-large-language-models-for-anxiety","slug":"evaluating-large-language-models-for-anxiety","title":"Evaluating Large Language Models for Anxiety and Depression Classification using Counseling and Psychotherapy Transcripts","date":"2024-07-18","arxiv_id":"2407.13228","n_code_links":1,"syntology":null},{"paper":"/paper/gpsformer-a-global-perception-and-local","slug":"gpsformer-a-global-perception-and-local","title":"GPSFormer: A Global Perception and Local Structure Fitting-based Transformer for Point Cloud Understanding","date":"2024-07-18","arxiv_id":"2407.13519","n_code_links":1,"syntology":null},{"paper":null,"slug":"hhgt-hierarchical-heterogeneous-graph","title":"HHGT: Hierarchical Heterogeneous Graph Transformer for Heterogeneous Graph Representation Learning","date":"2024-07-18","arxiv_id":"2407.13158","n_code_links":0,"syntology":null},{"paper":null,"slug":"large-language-models-as-reliable-knowledge","title":"How Reliable are LLMs as Knowledge Bases? Re-thinking Facutality and Consistency","date":"2024-07-18","arxiv_id":"2407.13578","n_code_links":0,"syntology":null},{"paper":null,"slug":"learning-from-mistakes-prompting-for","title":"Learning-From-Mistakes Prompting for Indigenous Language Translation","date":"2024-07-18","arxiv_id":"2407.13343","n_code_links":0,"syntology":null},{"paper":"/paper/oat-object-level-attention-transformer-for","slug":"oat-object-level-attention-transformer-for","title":"OAT: Object-Level Attention Transformer for Gaze Scanpath Prediction","date":"2024-07-18","arxiv_id":"2407.13335","n_code_links":1,"syntology":{"ran":15,"of":18,"n_ran_checked":15,"n_instrument":0,"unverified":3,"pointer_only":18,"phrase":"15 ran (of which 0 constructed an object rather than computing a result; 15 with no instrument failure: 1 honoured, 0 violated, 14 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","official":{"repos":["hkust-nisl/oat_eccv24"],"state":"official (archive's flag): 15 ran","n_ran":15,"n_constructed":0,"n_ran_no_instrument_failure":15,"n_unverified":3,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"pragyan-connecting-the-dots-in-tweets","title":"PRAGyan -- Connecting the Dots in Tweets","date":"2024-07-18","arxiv_id":"2407.13909","n_code_links":0,"syntology":null},{"paper":null,"slug":"qalam-a-multimodal-llm-for-arabic-optical","title":"Qalam : A Multimodal LLM for Arabic Optical Character and Handwriting Recognition","date":"2024-07-18","arxiv_id":"2407.13559","n_code_links":0,"syntology":null},{"paper":null,"slug":"reconstruct-the-pruned-model-without-any","title":"Reconstruct the Pruned Model without Any Retraining","date":"2024-07-18","arxiv_id":"2407.13331","n_code_links":0,"syntology":null},{"paper":null,"slug":"retrieval-augmented-generation-for-natural","title":"Retrieval-Augmented Generation for Natural Language Processing: A Survey","date":"2024-07-18","arxiv_id":"2407.13193","n_code_links":0,"syntology":null},{"paper":null,"slug":"retrieve-summarize-plan-advancing-multi-hop","title":"Retrieve, Summarize, Plan: Advancing Multi-hop Question Answering with an Iterative Approach","date":"2024-07-18","arxiv_id":"2407.13101","n_code_links":0,"syntology":null},{"paper":"/paper/revisiting-attention-for-multivariate-time","slug":"revisiting-attention-for-multivariate-time","title":"Revisiting Attention for Multivariate Time Series Forecasting","date":"2024-07-18","arxiv_id":"2407.13806","n_code_links":1,"syntology":null},{"paper":"/paper/similarity-over-factuality-are-we-making","slug":"similarity-over-factuality-are-we-making","title":"Similarity over Factuality: Are we making progress on multimodal out-of-context misinformation detection?","date":"2024-07-18","arxiv_id":"2407.13488","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":["stevejpapad/outcontext-misinfo-progress"],"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":"unified-egformer-exposure-guided-lightweight","title":"Unified-EGformer: Exposure Guided Lightweight Transformer for Mixed-Exposure Image Enhancement","date":"2024-07-18","arxiv_id":"2407.13170","n_code_links":0,"syntology":null},{"paper":"/paper/werewolf-arena-a-case-study-in-llm-evaluation","slug":"werewolf-arena-a-case-study-in-llm-evaluation","title":"Werewolf Arena: A Case Study in LLM Evaluation via Social Deduction","date":"2024-07-18","arxiv_id":"2407.13943","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":["google/werewolf_arena"],"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/why-do-you-cite-an-investigation-on-citation","slug":"why-do-you-cite-an-investigation-on-citation","title":"Why do you cite? An investigation on citation intents and decision-making classification processes","date":"2024-07-18","arxiv_id":"2407.13329","n_code_links":0,"syntology":null},{"paper":"/paper/adalog-post-training-quantization-for-vision","slug":"adalog-post-training-quantization-for-vision","title":"AdaLog: Post-Training Quantization for Vision Transformers with Adaptive Logarithm Quantizer","date":"2024-07-17","arxiv_id":"2407.12951","n_code_links":1,"syntology":{"ran":1,"of":2,"n_ran_checked":1,"n_instrument":0,"unverified":1,"pointer_only":0,"phrase":"1 ran (of which 1 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) · 1 unverified; the one sample that ran constructed an object rather than computing a result","official":{"repos":["GoatWu/AdaLog"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":"/paper/agentpoison-red-teaming-llm-agents-via","slug":"agentpoison-red-teaming-llm-agents-via","title":"AgentPoison: Red-teaming LLM Agents via Poisoning Memory or Knowledge Bases","date":"2024-07-17","arxiv_id":"2407.12784","n_code_links":1,"syntology":{"ran":16,"of":19,"n_ran_checked":15,"n_instrument":1,"unverified":3,"pointer_only":1,"phrase":"16 ran (of which 0 constructed an object rather than computing a result; 15 with no instrument failure: 1 honoured, 0 violated, 14 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","official":{"repos":["BillChan226/AgentPoison"],"state":"official (archive's flag): 16 ran","n_ran":16,"n_constructed":0,"n_ran_no_instrument_failure":15,"n_unverified":3,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"deep-learning-based-sentiment-analysis-of-1","title":"Deep Learning-based Sentiment Analysis of Olympics Tweets","date":"2024-07-17","arxiv_id":"2407.12376","n_code_links":0,"syntology":null},{"paper":"/paper/dual-hybrid-attention-network-for-specular","slug":"dual-hybrid-attention-network-for-specular","title":"Dual-Hybrid Attention Network for Specular Highlight Removal","date":"2024-07-17","arxiv_id":"2407.12255","n_code_links":1,"syntology":null},{"paper":null,"slug":"exploiting-inter-image-similarity-prior-for","title":"Exploiting Inter-Image Similarity Prior for Low-Bitrate Remote Sensing Image Compression","date":"2024-07-17","arxiv_id":"2407.12295","n_code_links":0,"syntology":null},{"paper":null,"slug":"facial-affect-recognition-based-on-multi","title":"Facial Affect Recognition based on Multi Architecture Encoder and Feature Fusion for the ABAW7 Challenge","date":"2024-07-17","arxiv_id":"2407.12258","n_code_links":0,"syntology":null},{"paper":"/paper/frequency-guidance-matters-skeletal-action","slug":"frequency-guidance-matters-skeletal-action","title":"Frequency Guidance Matters: Skeletal Action Recognition by Frequency-Aware Mixed Transformer","date":"2024-07-17","arxiv_id":"2407.12322","n_code_links":1,"syntology":null},{"paper":null,"slug":"hybrid-dynamic-pruning-a-pathway-to-efficient","title":"Hybrid Dynamic Pruning: A Pathway to Efficient Transformer Inference","date":"2024-07-17","arxiv_id":"2407.12893","n_code_links":0,"syntology":null},{"paper":null,"slug":"is-sarcasm-detection-a-step-by-step-reasoning","title":"Is Sarcasm Detection A Step-by-Step Reasoning Process in Large Language Models?","date":"2024-07-17","arxiv_id":"2407.12725","n_code_links":0,"syntology":null},{"paper":null,"slug":"lookupvit-compressing-visual-information-to-a","title":"LookupViT: Compressing visual information to a limited number of tokens","date":"2024-07-17","arxiv_id":"2407.12753","n_code_links":0,"syntology":null},{"paper":null,"slug":"on-initializing-transformers-with-pre-trained","title":"On Initializing Transformers with Pre-trained Embeddings","date":"2024-07-17","arxiv_id":"2407.12514","n_code_links":0,"syntology":null},{"paper":null,"slug":"optimizing-query-generation-for-enhanced","title":"Optimizing Query Generation for Enhanced Document Retrieval in RAG","date":"2024-07-17","arxiv_id":"2407.12325","n_code_links":0,"syntology":null},{"paper":"/paper/search-engines-llms-or-both-evaluating","slug":"search-engines-llms-or-both-evaluating","title":"Evaluating Search Engines and Large Language Models for Answering Health Questions","date":"2024-07-17","arxiv_id":"2407.12468","n_code_links":1,"syntology":null},{"paper":"/paper/sharif-str-at-semeval-2024-task-1-transformer","slug":"sharif-str-at-semeval-2024-task-1-transformer","title":"Sharif-STR at SemEval-2024 Task 1: Transformer as a Regression Model for Fine-Grained Scoring of Textual Semantic Relations","date":"2024-07-17","arxiv_id":"2407.12426","n_code_links":1,"syntology":null},{"paper":null,"slug":"temporal-label-hierachical-network-for","title":"Temporal Label Hierachical Network for Compound Emotion Recognition","date":"2024-07-17","arxiv_id":"2407.12973","n_code_links":0,"syntology":null},{"paper":"/paper/text-and-feature-based-models-for-compound","slug":"text-and-feature-based-models-for-compound","title":"Textualized and Feature-based Models for Compound Multimodal Emotion Recognition in the Wild","date":"2024-07-17","arxiv_id":"2407.12927","n_code_links":2,"syntology":{"ran":3,"of":4,"n_ran_checked":3,"n_instrument":0,"unverified":1,"pointer_only":4,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","official":{"repos":["nicolas-richet/feature-vs-text-compound-emotion"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"a-graph-based-adversarial-imitation-learning","title":"A Graph-based Adversarial Imitation Learning Framework for Reliable & Realtime Fleet Scheduling in Urban Air Mobility","date":"2024-07-16","arxiv_id":"2407.12113","n_code_links":0,"syntology":null},{"paper":"/paper/better-rag-using-relevant-information-gain","slug":"better-rag-using-relevant-information-gain","title":"Better RAG using Relevant Information Gain","date":"2024-07-16","arxiv_id":"2407.12101","n_code_links":1,"syntology":null},{"paper":null,"slug":"beyond-binary-multiclass-paraphasia-detection","title":"Beyond Binary: Multiclass Paraphasia Detection with Generative Pretrained Transformers and End-to-End Models","date":"2024-07-16","arxiv_id":"2407.11345","n_code_links":0,"syntology":null},{"paper":null,"slug":"chatbcg-can-ai-read-your-slide-deck","title":"ChatBCG: Can AI Read Your Slide Deck?","date":"2024-07-16","arxiv_id":"2407.12875","n_code_links":0,"syntology":null},{"paper":null,"slug":"co-designing-binarized-transformer-and","title":"Co-Designing Binarized Transformer and Hardware Accelerator for Efficient End-to-End Edge Deployment","date":"2024-07-16","arxiv_id":"2407.12070","n_code_links":0,"syntology":null},{"paper":null,"slug":"dino-diffusion-scaling-medical-diffusion-via","title":"DiNO-Diffusion. Scaling Medical Diffusion via Self-Supervised Pre-Training","date":"2024-07-16","arxiv_id":"2407.11594","n_code_links":0,"syntology":null},{"paper":"/paper/does-refusal-training-in-llms-generalize-to","slug":"does-refusal-training-in-llms-generalize-to","title":"Does Refusal Training in LLMs Generalize to the Past Tense?","date":"2024-07-16","arxiv_id":"2407.11969","n_code_links":1,"syntology":{"ran":3,"of":3,"n_ran_checked":3,"n_instrument":0,"unverified":0,"pointer_only":3,"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":["tml-epfl/llm-past-tense"],"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":"/paper/ecoh-turn-level-coherence-evaluation-for","slug":"ecoh-turn-level-coherence-evaluation-for","title":"ECoh: Turn-level Coherence Evaluation for Multilingual Dialogues","date":"2024-07-16","arxiv_id":"2407.11660","n_code_links":1,"syntology":null},{"paper":null,"slug":"educational-personalized-learning-path","title":"Educational Personalized Learning Path Planning with Large Language Models","date":"2024-07-16","arxiv_id":"2407.11773","n_code_links":0,"syntology":null},{"paper":"/paper/exploring-quantization-for-efficient-pre","slug":"exploring-quantization-for-efficient-pre","title":"Exploring Quantization for Efficient Pre-Training of Transformer Language Models","date":"2024-07-16","arxiv_id":"2407.11722","n_code_links":1,"syntology":null},{"paper":null,"slug":"gpt-assisted-annotation-of-rhetorical-and","title":"GPT Assisted Annotation of Rhetorical and Linguistic Features for Interpretable Propaganda Technique Detection in News Text","date":"2024-07-16","arxiv_id":"2407.11827","n_code_links":0,"syntology":null},{"paper":"/paper/hierarchical-separable-video-transformer-for","slug":"hierarchical-separable-video-transformer-for","title":"Hierarchical Separable Video Transformer for Snapshot Compressive Imaging","date":"2024-07-16","arxiv_id":"2407.11946","n_code_links":1,"syntology":null},{"paper":"/paper/lami-detr-open-vocabulary-detection-with","slug":"lami-detr-open-vocabulary-detection-with","title":"LaMI-DETR: Open-Vocabulary Detection with Language Model Instruction","date":"2024-07-16","arxiv_id":"2407.11335","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 2 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","official":{"repos":["eternaldolphin/lami-detr"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"large-language-models-as-misleading","title":"Large Language Models as Misleading Assistants in Conversation","date":"2024-07-16","arxiv_id":"2407.11789","n_code_links":0,"syntology":null},{"paper":null,"slug":"large-visual-language-models-are-also-good","title":"Large Visual-Language Models Are Also Good Classifiers: A Study of In-Context Multimodal Fake News Detection","date":"2024-07-16","arxiv_id":"2407.12879","n_code_links":0,"syntology":null},{"paper":null,"slug":"learning-global-and-local-features-of-power","title":"Learning Global and Local Features of Power Load Series Through Transformer and 2D-CNN: An Image-based Multi-step Forecasting Approach Incorporating Phase Space Reconstruction","date":"2024-07-16","arxiv_id":"2407.11553","n_code_links":0,"syntology":null},{"paper":null,"slug":"llms-in-the-loop-part-1-expert-small-ai","title":"LLMs-in-the-loop Part-1: Expert Small AI Models for Bio-Medical Text Translation","date":"2024-07-16","arxiv_id":"2407.12126","n_code_links":0,"syntology":null},{"paper":"/paper/lofti-localization-and-factuality-transfer-to","slug":"lofti-localization-and-factuality-transfer-to","title":"LoFTI: Localization and Factuality Transfer to Indian Locales","date":"2024-07-16","arxiv_id":"2407.11833","n_code_links":1,"syntology":null},{"paper":"/paper/lora-pt-low-rank-adapting-unetr-for","slug":"lora-pt-low-rank-adapting-unetr-for","title":"LoRA-PT: Low-Rank Adapting UNETR for Hippocampus Segmentation Using Principal Tensor Singular Values and Vectors","date":"2024-07-16","arxiv_id":"2407.11292","n_code_links":1,"syntology":null},{"paper":null,"slug":"lrq-optimizing-post-training-quantization-for","title":"LRQ: Optimizing Post-Training Quantization for Large Language Models by Learning Low-Rank Weight-Scaling Matrices","date":"2024-07-16","arxiv_id":"2407.11534","n_code_links":0,"syntology":null},{"paper":null,"slug":"mindful-rag-a-study-of-points-of-failure-in","title":"Mindful-RAG: A Study of Points of Failure in Retrieval Augmented Generation","date":"2024-07-16","arxiv_id":"2407.12216","n_code_links":0,"syntology":null},{"paper":null,"slug":"probing-the-efficacy-of-federated-parameter","title":"Probing the Efficacy of Federated Parameter-Efficient Fine-Tuning of Vision Transformers for Medical Image Classification","date":"2024-07-16","arxiv_id":"2407.11573","n_code_links":0,"syntology":null},{"paper":null,"slug":"r-sfllm-jamming-resilient-framework-for-split","title":"R-SFLLM: Jamming Resilient Framework for Split Federated Learning with Large Language Models","date":"2024-07-16","arxiv_id":"2407.11654","n_code_links":0,"syntology":null},{"paper":"/paper/relation-detr-exploring-explicit-position","slug":"relation-detr-exploring-explicit-position","title":"Relation DETR: Exploring Explicit Position Relation Prior for Object Detection","date":"2024-07-16","arxiv_id":"2407.11699","n_code_links":2,"syntology":{"ran":1,"of":1,"n_ran_checked":1,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["xiuqhou/relation-detr"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"representation-bias-in-political-sample","title":"Representation Bias in Political Sample Simulations with Large Language Models","date":"2024-07-16","arxiv_id":"2407.11409","n_code_links":0,"syntology":null},{"paper":null,"slug":"rethinking-transformer-based-multi-document","title":"Rethinking Transformer-based Multi-document Summarization: An Empirical Investigation","date":"2024-07-16","arxiv_id":"2407.11948","n_code_links":0,"syntology":null},{"paper":null,"slug":"review-feedback-reason-refer-a-novel","title":"ReFeR: Improving Evaluation and Reasoning through Hierarchy of Models","date":"2024-07-16","arxiv_id":"2407.12877","n_code_links":0,"syntology":null},{"paper":"/paper/scaling-diffusion-transformers-to-16-billion","slug":"scaling-diffusion-transformers-to-16-billion","title":"Scaling Diffusion Transformers to 16 Billion Parameters","date":"2024-07-16","arxiv_id":"2407.11633","n_code_links":1,"syntology":{"ran":12,"of":16,"n_ran_checked":6,"n_instrument":6,"unverified":4,"pointer_only":16,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 4 honoured, 1 violated, 1 with no contract checked; 6 where Syntology's instrument failed) · 4 unverified","official":{"repos":["feizc/dit-moe"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":4,"ran_from_kinds":["official"]}}},{"paper":"/paper/scientific-qa-system-with-verifiable-answers","slug":"scientific-qa-system-with-verifiable-answers","title":"Scientific QA System with Verifiable Answers","date":"2024-07-16","arxiv_id":"2407.11485","n_code_links":1,"syntology":null},{"paper":"/paper/sgiformer-semantic-guided-and-geometric","slug":"sgiformer-semantic-guided-and-geometric","title":"SGIFormer: Semantic-guided and Geometric-enhanced Interleaving Transformer for 3D Instance Segmentation","date":"2024-07-16","arxiv_id":"2407.11564","n_code_links":1,"syntology":null},{"paper":null,"slug":"siamese-transformer-networks-for-few-shot","title":"Siamese Transformer Networks for Few-shot Image Classification","date":"2024-07-16","arxiv_id":"2408.01427","n_code_links":0,"syntology":null},{"paper":"/paper/tcformer-visual-recognition-via-token","slug":"tcformer-visual-recognition-via-token","title":"TCFormer: Visual Recognition via Token Clustering Transformer","date":"2024-07-16","arxiv_id":"2407.11321","n_code_links":1,"syntology":null},{"paper":"/paper/trust-no-bot-discovering-personal-disclosures","slug":"trust-no-bot-discovering-personal-disclosures","title":"Trust No Bot: Discovering Personal Disclosures in Human-LLM Conversations in the Wild","date":"2024-07-16","arxiv_id":"2407.11438","n_code_links":1,"syntology":null},{"paper":"/paper/video-language-alignment-pre-training-via","slug":"video-language-alignment-pre-training-via","title":"Video-Language Alignment via Spatio-Temporal Graph Transformer","date":"2024-07-16","arxiv_id":"2407.11677","n_code_links":1,"syntology":null},{"paper":"/paper/aligning-neuronal-coding-of-dynamic-visual","slug":"aligning-neuronal-coding-of-dynamic-visual","title":"Aligning Neuronal Coding of Dynamic Visual Scenes with Foundation Vision Models","date":"2024-07-15","arxiv_id":"2407.10737","n_code_links":1,"syntology":null},{"paper":"/paper/an-empirical-study-of-mamba-based-pedestrian","slug":"an-empirical-study-of-mamba-based-pedestrian","title":"An Empirical Study of Mamba-based Pedestrian Attribute Recognition","date":"2024-07-15","arxiv_id":"2407.10374","n_code_links":1,"syntology":null},{"paper":null,"slug":"anticipating-future-object-compositions","title":"Anticipating Future Object Compositions without Forgetting","date":"2024-07-15","arxiv_id":"2407.10723","n_code_links":0,"syntology":null},{"paper":null,"slug":"bandcontrolnet-parallel-transformers-based","title":"BandControlNet: Parallel Transformers-based Steerable Popular Music Generation with Fine-Grained Spatiotemporal Features","date":"2024-07-15","arxiv_id":"2407.10462","n_code_links":0,"syntology":null},{"paper":null,"slug":"beyond-generative-artificial-intelligence","title":"Beyond Generative Artificial Intelligence: Roadmap for Natural Language Generation","date":"2024-07-15","arxiv_id":"2407.10554","n_code_links":0,"syntology":null},{"paper":null,"slug":"clave-an-adaptive-framework-for-evaluating","title":"CLAVE: An Adaptive Framework for Evaluating Values of LLM Generated Responses","date":"2024-07-15","arxiv_id":"2407.10725","n_code_links":0,"syntology":null},{"paper":"/paper/communication-and-computation-efficient","slug":"communication-and-computation-efficient","title":"Communication- and Computation-Efficient Distributed Submodular Optimization in Robot Mesh Networks","date":"2024-07-15","arxiv_id":"2407.10382","n_code_links":1,"syntology":null},{"paper":null,"slug":"deep-learning-based-operators-for","title":"Deep Learning-Based Operators for Evolutionary Algorithms","date":"2024-07-15","arxiv_id":"2407.10477","n_code_links":0,"syntology":null},{"paper":null,"slug":"deepgate3-towards-scalable-circuit","title":"DeepGate3: Towards Scalable Circuit Representation Learning","date":"2024-07-15","arxiv_id":"2407.11095","n_code_links":0,"syntology":null},{"paper":null,"slug":"empowering-llms-for-verilog-generation","title":"CodeV: Empowering LLMs with HDL Generation through Multi-Level Summarization","date":"2024-07-15","arxiv_id":"2407.10424","n_code_links":0,"syntology":null},{"paper":"/paper/enhancing-retrieval-and-managing-retrieval-a","slug":"enhancing-retrieval-and-managing-retrieval-a","title":"Enhancing Retrieval and Managing Retrieval: A Four-Module Synergy for Improved Quality and Efficiency in RAG Systems","date":"2024-07-15","arxiv_id":"2407.10670","n_code_links":1,"syntology":{"ran":9,"of":11,"n_ran_checked":9,"n_instrument":0,"unverified":2,"pointer_only":11,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 1 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","official":{"repos":["ancientshi/erm4"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"evaluation-of-rag-metrics-for-question","title":"Evaluation of RAG Metrics for Question Answering in the Telecom Domain","date":"2024-07-15","arxiv_id":"2407.12873","n_code_links":0,"syntology":null},{"paper":null,"slug":"exploring-the-potentials-and-challenges-of","title":"Exploring the Potentials and Challenges of Deep Generative Models in Product Design Conception","date":"2024-07-15","arxiv_id":"2407.11104","n_code_links":0,"syntology":null},{"paper":null,"slug":"foundational-autoraters-taming-large-language","title":"Foundational Autoraters: Taming Large Language Models for Better Automatic Evaluation","date":"2024-07-15","arxiv_id":"2407.10817","n_code_links":0,"syntology":null},{"paper":null,"slug":"gpt-sonograpy-hand-gesture-decoding-from","title":"GPT Sonograpy: Hand Gesture Decoding from Forearm Ultrasound Images via VLM","date":"2024-07-15","arxiv_id":"2407.10870","n_code_links":0,"syntology":null},{"paper":null,"slug":"gtpt-group-based-token-pruning-transformer","title":"GTPT: Group-based Token Pruning Transformer for Efficient Human Pose Estimation","date":"2024-07-15","arxiv_id":"2407.10756","n_code_links":0,"syntology":null},{"paper":null,"slug":"human-centric-transformer-for-domain-adaptive","title":"Human-Centric Transformer for Domain Adaptive Action Recognition","date":"2024-07-15","arxiv_id":"2407.10860","n_code_links":0,"syntology":null},{"paper":null,"slug":"learning-natural-consistency-representation","title":"Learning Natural Consistency Representation for Face Forgery Video Detection","date":"2024-07-15","arxiv_id":"2407.10550","n_code_links":0,"syntology":null},{"paper":null,"slug":"leveraging-llm-respondents-for-item","title":"Leveraging LLM-Respondents for Item Evaluation: a Psychometric Analysis","date":"2024-07-15","arxiv_id":"2407.10899","n_code_links":0,"syntology":null},{"paper":null,"slug":"making-new-connections-llms-as-puzzle","title":"Making New Connections: LLMs as Puzzle Generators for The New York Times' Connections Word Game","date":"2024-07-15","arxiv_id":"2407.11240","n_code_links":0,"syntology":null},{"paper":null,"slug":"mechanistic-interpretability-of-large","title":"Mechanistic interpretability of large language models with applications to the financial services industry","date":"2024-07-15","arxiv_id":"2407.11215","n_code_links":0,"syntology":null},{"paper":"/paper/metallm-a-high-performant-and-cost-efficient","slug":"metallm-a-high-performant-and-cost-efficient","title":"MetaLLM: A High-performant and Cost-efficient Dynamic Framework for Wrapping LLMs","date":"2024-07-15","arxiv_id":"2407.10834","n_code_links":1,"syntology":{"ran":6,"of":7,"n_ran_checked":6,"n_instrument":0,"unverified":1,"pointer_only":7,"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) · 1 unverified","official":{"repos":["mail-research/metallm-wrapper"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":"/paper/no-train-all-gain-self-supervised-gradients","slug":"no-train-all-gain-self-supervised-gradients","title":"No Train, all Gain: Self-Supervised Gradients Improve Deep Frozen Representations","date":"2024-07-15","arxiv_id":"2407.10964","n_code_links":1,"syntology":{"ran":7,"of":12,"n_ran_checked":5,"n_instrument":2,"unverified":5,"pointer_only":0,"phrase":"7 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; 2 where Syntology's instrument failed) · 5 unverified","official":{"repos":["waltersimoncini/fungivision"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":5,"ran_from_kinds":["official"]}}},{"paper":"/paper/ovlw-detr-open-vocabulary-light-weighted","slug":"ovlw-detr-open-vocabulary-light-weighted","title":"OVLW-DETR: Open-Vocabulary Light-Weighted Detection Transformer","date":"2024-07-15","arxiv_id":"2407.10655","n_code_links":1,"syntology":null},{"paper":"/paper/pathformer3d-a-3d-scanpath-transformer-for","slug":"pathformer3d-a-3d-scanpath-transformer-for","title":"Pathformer3D: A 3D Scanpath Transformer for 360° Images","date":"2024-07-15","arxiv_id":"2407.10563","n_code_links":1,"syntology":null},{"paper":"/paper/polyroom-room-aware-transformer-for-floorplan","slug":"polyroom-room-aware-transformer-for-floorplan","title":"PolyRoom: Room-aware Transformer for Floorplan Reconstruction","date":"2024-07-15","arxiv_id":"2407.10439","n_code_links":1,"syntology":null},{"paper":"/paper/representing-rule-based-chatbots-with","slug":"representing-rule-based-chatbots-with","title":"Representing Rule-based Chatbots with Transformers","date":"2024-07-15","arxiv_id":"2407.10949","n_code_links":1,"syntology":null},{"paper":"/paper/seed-a-simple-and-effective-3d-detr-in-point","slug":"seed-a-simple-and-effective-3d-detr-in-point","title":"SEED: A Simple and Effective 3D DETR in Point Clouds","date":"2024-07-15","arxiv_id":"2407.10749","n_code_links":1,"syntology":{"ran":8,"of":10,"n_ran_checked":7,"n_instrument":1,"unverified":2,"pointer_only":10,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","official":{"repos":["happinesslz/seed"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":"/paper/sibyl-simple-yet-effective-agent-framework","slug":"sibyl-simple-yet-effective-agent-framework","title":"Sibyl: Simple yet Effective Agent Framework for Complex Real-world Reasoning","date":"2024-07-15","arxiv_id":"2407.10718","n_code_links":1,"syntology":{"ran":3,"of":3,"n_ran_checked":3,"n_instrument":0,"unverified":0,"pointer_only":3,"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":["ag2s1/sibyl-system"],"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"]}}}],"record_sha256":"a7bb265d24b3cfd79845a4a43e5db7061b068871a107019038e32c1787d44333","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}