{"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/residual-connection/papers/74","list_of":"/method/residual-connection","method":"Residual Connection","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":74,"pages_in_order":285,"rows_per_page":100,"rows":[7301,7400],"of":28401,"counts":{"archive_papers_tagged":28401,"with_a_code_link":12847,"where_syntology_ran_a_sample":3897,"not_listed_spam_title":0,"listed":28401,"listed_where_code_ran":3897,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":3291,"every_run_a_failure_of_syntologys_instrument":606,"listed_with_a_run_with_no_instrument_failure":3291,"listed_every_run_a_failure_of_syntologys_instrument":606,"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/residual-connection","prev":"/method/residual-connection/papers/73","next":"/method/residual-connection/papers/75","papers":[{"paper":"/paper/open-source-language-models-can-provide","slug":"open-source-language-models-can-provide","title":"Open Source Language Models Can Provide Feedback: Evaluating LLMs' Ability to Help Students Using GPT-4-As-A-Judge","date":"2024-05-08","arxiv_id":"2405.05253","n_code_links":1,"syntology":null},{"paper":null,"slug":"seeds-of-stereotypes-a-large-scale-textual","title":"Seeds of Stereotypes: A Large-Scale Textual Analysis of Race and Gender Associations with Diseases in Online Sources","date":"2024-05-08","arxiv_id":"2405.05049","n_code_links":0,"syntology":null},{"paper":null,"slug":"utilizing-large-language-models-to-generate","title":"Utilizing Large Language Models to Generate Synthetic Data to Increase the Performance of BERT-Based Neural Networks","date":"2024-05-08","arxiv_id":"2405.06695","n_code_links":0,"syntology":null},{"paper":"/paper/you-only-cache-once-decoder-decoder","slug":"you-only-cache-once-decoder-decoder","title":"You Only Cache Once: Decoder-Decoder Architectures for Language Models","date":"2024-05-08","arxiv_id":"2405.05254","n_code_links":1,"syntology":null},{"paper":null,"slug":"an-advanced-features-extraction-module-for","title":"An Advanced Features Extraction Module for Remote Sensing Image Super-Resolution","date":"2024-05-07","arxiv_id":"2405.04595","n_code_links":0,"syntology":null},{"paper":null,"slug":"an-llm-tool-compiler-for-fused-parallel","title":"An LLM-Tool Compiler for Fused Parallel Function Calling","date":"2024-05-07","arxiv_id":"2405.17438","n_code_links":0,"syntology":null},{"paper":"/paper/d-trattunet-toward-hybrid-cnn-transformer","slug":"d-trattunet-toward-hybrid-cnn-transformer","title":"D-TrAttUnet: Toward Hybrid CNN-Transformer Architecture for Generic and Subtle Segmentation in Medical Images","date":"2024-05-07","arxiv_id":"2405.04169","n_code_links":1,"syntology":null},{"paper":"/paper/enhancing-the-efficiency-and-accuracy-of","slug":"enhancing-the-efficiency-and-accuracy-of","title":"Enhancing the Efficiency and Accuracy of Underlying Asset Reviews in Structured Finance: The Application of Multi-agent Framework","date":"2024-05-07","arxiv_id":"2405.04294","n_code_links":1,"syntology":null},{"paper":"/paper/enriched-bert-embeddings-for-scholarly","slug":"enriched-bert-embeddings-for-scholarly","title":"Enriched BERT Embeddings for Scholarly Publication Classification","date":"2024-05-07","arxiv_id":"2405.04136","n_code_links":1,"syntology":null},{"paper":null,"slug":"eratta-extreme-rag-for-table-to-answers-with","title":"ERATTA: Extreme RAG for Table To Answers with Large Language Models","date":"2024-05-07","arxiv_id":"2405.03963","n_code_links":0,"syntology":null},{"paper":null,"slug":"evaluating-text-summaries-generated-by-large","title":"Evaluating Text Summaries Generated by Large Language Models Using OpenAI's GPT","date":"2024-05-07","arxiv_id":"2405.04053","n_code_links":0,"syntology":null},{"paper":"/paper/exploring-explainable-ai-techniques-for","slug":"exploring-explainable-ai-techniques-for","title":"Exploring Explainable AI Techniques for Improved Interpretability in Lung and Colon Cancer Classification","date":"2024-05-07","arxiv_id":"2405.04610","n_code_links":1,"syntology":null},{"paper":null,"slug":"folded-context-condensation-in-path-integral","title":"Folded Context Condensation in Path Integral Formalism for Infinite Context Transformers","date":"2024-05-07","arxiv_id":"2405.04620","n_code_links":0,"syntology":null},{"paper":null,"slug":"gpt-enabled-cybersecurity-training-a-tailored","title":"GPT-Enabled Cybersecurity Training: A Tailored Approach for Effective Awareness","date":"2024-05-07","arxiv_id":"2405.04138","n_code_links":0,"syntology":null},{"paper":null,"slug":"hafformer-a-hierarchical-attention-free","title":"HAFFormer: A Hierarchical Attention-Free Framework for Alzheimer's Disease Detection From Spontaneous Speech","date":"2024-05-07","arxiv_id":"2405.03952","n_code_links":0,"syntology":null},{"paper":"/paper/how-does-gpt-2-predict-acronyms-extracting","slug":"how-does-gpt-2-predict-acronyms-extracting","title":"How does GPT-2 Predict Acronyms? Extracting and Understanding a Circuit via Mechanistic Interpretability","date":"2024-05-07","arxiv_id":"2405.04156","n_code_links":1,"syntology":{"ran":3,"of":4,"n_ran_checked":1,"n_instrument":2,"unverified":1,"pointer_only":4,"phrase":"3 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; 2 where Syntology's instrument failed) · 1 unverified","official":{"repos":["jgcarrasco/acronyms_paper"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":"/paper/learning-linear-block-error-correction-codes","slug":"learning-linear-block-error-correction-codes","title":"Learning Linear Block Error Correction Codes","date":"2024-05-07","arxiv_id":"2405.04050","n_code_links":1,"syntology":null},{"paper":null,"slug":"long-context-alignment-with-short","title":"Long Context Alignment with Short Instructions and Synthesized Positions","date":"2024-05-07","arxiv_id":"2405.03939","n_code_links":0,"syntology":null},{"paper":null,"slug":"masked-graph-transformer-for-large-scale","title":"Masked Graph Transformer for Large-Scale Recommendation","date":"2024-05-07","arxiv_id":"2405.04028","n_code_links":0,"syntology":null},{"paper":"/paper/medvoc-vocabulary-adaptation-for-fine-tuning","slug":"medvoc-vocabulary-adaptation-for-fine-tuning","title":"MEDVOC: Vocabulary Adaptation for Fine-tuning Pre-trained Language Models on Medical Text Summarization","date":"2024-05-07","arxiv_id":"2405.04163","n_code_links":1,"syntology":{"ran":8,"of":9,"n_ran_checked":8,"n_instrument":0,"unverified":1,"pointer_only":9,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 1 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","official":{"repos":["gb-kgp/medvoc"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":"/paper/naturalcodebench-examining-coding-performance","slug":"naturalcodebench-examining-coding-performance","title":"NaturalCodeBench: Examining Coding Performance Mismatch on HumanEval and Natural User Prompts","date":"2024-05-07","arxiv_id":"2405.04520","n_code_links":1,"syntology":null},{"paper":null,"slug":"pov-learning-individual-alignment-of","title":"POV Learning: Individual Alignment of Multimodal Models using Human Perception","date":"2024-05-07","arxiv_id":"2405.04443","n_code_links":0,"syntology":null},{"paper":"/paper/predictive-modeling-with-temporal-graphical","slug":"predictive-modeling-with-temporal-graphical","title":"Predictive Modeling with Temporal Graphical Representation on Electronic Health Records","date":"2024-05-07","arxiv_id":"2405.03943","n_code_links":1,"syntology":{"ran":10,"of":10,"n_ran_checked":10,"n_instrument":0,"unverified":0,"pointer_only":10,"phrase":"10 ran (of which 3 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) · 0 unverified","official":{"repos":["the-real-jerrychen/trans"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":3,"n_ran_no_instrument_failure":10,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"remote-diffusion","title":"Remote Diffusion","date":"2024-05-07","arxiv_id":"2405.04717","n_code_links":0,"syntology":null},{"paper":"/paper/revisiting-character-level-adversarial","slug":"revisiting-character-level-adversarial","title":"Revisiting Character-level Adversarial Attacks for Language Models","date":"2024-05-07","arxiv_id":"2405.04346","n_code_links":1,"syntology":{"ran":23,"of":31,"n_ran_checked":13,"n_instrument":10,"unverified":8,"pointer_only":0,"phrase":"23 ran (of which 3 constructed an object rather than computing a result; 13 with no instrument failure: 0 honoured, 0 violated, 13 with no contract checked; 10 where Syntology's instrument failed) · 8 unverified","official":{"repos":["lions-epfl/charmer"],"state":"official (archive's flag): 23 ran","n_ran":23,"n_constructed":3,"n_ran_no_instrument_failure":13,"n_unverified":8,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"robust-implementation-of-retrieval-augmented","title":"Robust Implementation of Retrieval-Augmented Generation on Edge-based Computing-in-Memory Architectures","date":"2024-05-07","arxiv_id":"2405.04700","n_code_links":0,"syntology":null},{"paper":null,"slug":"s3former-self-supervised-high-resolution","title":"S3Former: Self-supervised High-resolution Transformer for Solar PV Profiling","date":"2024-05-07","arxiv_id":"2405.04489","n_code_links":0,"syntology":null},{"paper":"/paper/structured-click-control-in-transformer-based","slug":"structured-click-control-in-transformer-based","title":"Structured Click Control in Transformer-based Interactive Segmentation","date":"2024-05-07","arxiv_id":"2405.04009","n_code_links":1,"syntology":{"ran":8,"of":9,"n_ran_checked":6,"n_instrument":2,"unverified":1,"pointer_only":9,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 1 honoured, 0 violated, 5 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","official":{"repos":["hahamyt/scc"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"sutra-scalable-multilingual-language-model","title":"SUTRA: Scalable Multilingual Language Model Architecture","date":"2024-05-07","arxiv_id":"2405.06694","n_code_links":0,"syntology":null},{"paper":null,"slug":"the-silicone-ceiling-auditing-gpt-s-race-and","title":"The Silicon Ceiling: Auditing GPT's Race and Gender Biases in Hiring","date":"2024-05-07","arxiv_id":"2405.04412","n_code_links":0,"syntology":null},{"paper":null,"slug":"utilizing-gpt-to-enhance-text-summarization-a","title":"Utilizing GPT to Enhance Text Summarization: A Strategy to Minimize Hallucinations","date":"2024-05-07","arxiv_id":"2405.04039","n_code_links":0,"syntology":null},{"paper":"/paper/vision-mamba-a-comprehensive-survey-and","slug":"vision-mamba-a-comprehensive-survey-and","title":"Vision Mamba: A Comprehensive Survey and Taxonomy","date":"2024-05-07","arxiv_id":"2405.04404","n_code_links":1,"syntology":null},{"paper":"/paper/xlstm-extended-long-short-term-memory","slug":"xlstm-extended-long-short-term-memory","title":"xLSTM: Extended Long Short-Term Memory","date":"2024-05-07","arxiv_id":"2405.04517","n_code_links":5,"syntology":{"ran":7,"of":9,"n_ran_checked":7,"n_instrument":0,"unverified":2,"pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","official":{"repos":["nx-ai/xlstm"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":"/paper/alphamath-almost-zero-process-supervision","slug":"alphamath-almost-zero-process-supervision","title":"AlphaMath Almost Zero: Process Supervision without Process","date":"2024-05-06","arxiv_id":"2405.03553","n_code_links":1,"syntology":null},{"paper":"/paper/anchored-answers-unravelling-positional-bias","slug":"anchored-answers-unravelling-positional-bias","title":"Anchored Answers: Unravelling Positional Bias in GPT-2's Multiple-Choice Questions","date":"2024-05-06","arxiv_id":"2405.03205","n_code_links":1,"syntology":null},{"paper":null,"slug":"characterizing-the-dilemma-of-performance-and","title":"Characterizing the Dilemma of Performance and Index Size in Billion-Scale Vector Search and Breaking It with Second-Tier Memory","date":"2024-05-06","arxiv_id":"2405.03267","n_code_links":0,"syntology":null},{"paper":null,"slug":"class-relevant-patch-embedding-selection-for","title":"Class-relevant Patch Embedding Selection for Few-Shot Image Classification","date":"2024-05-06","arxiv_id":"2405.03722","n_code_links":0,"syntology":null},{"paper":null,"slug":"compressing-long-context-for-enhancing-rag","title":"Compressing Long Context for Enhancing RAG with AMR-based Concept Distillation","date":"2024-05-06","arxiv_id":"2405.03085","n_code_links":0,"syntology":null},{"paper":"/paper/cra5-extreme-compression-of-era5-for-portable","slug":"cra5-extreme-compression-of-era5-for-portable","title":"CRA5: Extreme Compression of ERA5 for Portable Global Climate and Weather Research via an Efficient Variational Transformer","date":"2024-05-06","arxiv_id":"2405.03376","n_code_links":1,"syntology":null},{"paper":null,"slug":"deep-learning-classifier-of-locally-advanced","title":"Swin transformers are robust to distribution and concept drift in endoscopy-based longitudinal rectal cancer assessment","date":"2024-05-06","arxiv_id":"2405.03762","n_code_links":0,"syntology":null},{"paper":null,"slug":"detecting-android-malware-from-neural","title":"Detecting Android Malware: From Neural Embeddings to Hands-On Validation with BERTroid","date":"2024-05-06","arxiv_id":"2405.03620","n_code_links":0,"syntology":null},{"paper":null,"slug":"detecting-anti-semitic-hate-speech-using","title":"Detecting Anti-Semitic Hate Speech using Transformer-based Large Language Models","date":"2024-05-06","arxiv_id":"2405.03794","n_code_links":0,"syntology":null},{"paper":null,"slug":"dual-relation-mining-network-for-zero-shot","title":"Dual Relation Mining Network for Zero-Shot Learning","date":"2024-05-06","arxiv_id":"2405.03613","n_code_links":0,"syntology":null},{"paper":null,"slug":"enhancing-detrs-variants-through-improved","title":"Enhancing DETRs Variants through Improved Content Query and Similar Query Aggregation","date":"2024-05-06","arxiv_id":"2405.03318","n_code_links":0,"syntology":null},{"paper":"/paper/eragent-enhancing-retrieval-augmented","slug":"eragent-enhancing-retrieval-augmented","title":"ERAGent: Enhancing Retrieval-Augmented Language Models with Improved Accuracy, Efficiency, and Personalization","date":"2024-05-06","arxiv_id":"2405.06683","n_code_links":1,"syntology":null},{"paper":null,"slug":"exploring-the-frontiers-of-softmax-provable","title":"Exploring the Frontiers of Softmax: Provable Optimization, Applications in Diffusion Model, and Beyond","date":"2024-05-06","arxiv_id":"2405.03251","n_code_links":0,"syntology":null},{"paper":null,"slug":"green-generative-radiology-report-evaluation","title":"GREEN: Generative Radiology Report Evaluation and Error Notation","date":"2024-05-06","arxiv_id":"2405.03595","n_code_links":0,"syntology":null},{"paper":"/paper/hire-me-or-not-examining-language-model-s","slug":"hire-me-or-not-examining-language-model-s","title":"Hire Me or Not? Examining Language Model's Behavior with Occupation Attributes","date":"2024-05-06","arxiv_id":"2405.06687","n_code_links":1,"syntology":{"ran":10,"of":13,"n_ran_checked":10,"n_instrument":0,"unverified":3,"pointer_only":13,"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) · 3 unverified","official":{"repos":["daminz97/multi-step_gsv"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":3,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"intra-task-mutual-attention-based-vision","title":"Intra-task Mutual Attention based Vision Transformer for Few-Shot Learning","date":"2024-05-06","arxiv_id":"2405.03109","n_code_links":0,"syntology":null},{"paper":null,"slug":"investigating-personalized-driving-behaviors","title":"Investigating Personalized Driving Behaviors in Dilemma Zones: Analysis and Prediction of Stop-or-Go Decisions","date":"2024-05-06","arxiv_id":"2405.03873","n_code_links":0,"syntology":null},{"paper":"/paper/large-language-models-reveal-information","slug":"large-language-models-reveal-information","title":"Large Language Models Reveal Information Operation Goals, Tactics, and Narrative Frames","date":"2024-05-06","arxiv_id":"2405.03688","n_code_links":1,"syntology":null},{"paper":null,"slug":"mammoth2-scaling-instructions-from-the-web","title":"MAmmoTH2: Scaling Instructions from the Web","date":"2024-05-06","arxiv_id":"2405.03548","n_code_links":0,"syntology":null},{"paper":null,"slug":"modality-prompts-for-arbitrary-modality","title":"Modality Prompts for Arbitrary Modality Salient Object Detection","date":"2024-05-06","arxiv_id":"2405.03351","n_code_links":0,"syntology":null},{"paper":"/paper/recycle-fast-and-efficient-long-time-series","slug":"recycle-fast-and-efficient-long-time-series","title":"ReCycle: Fast and Efficient Long Time Series Forecasting with Residual Cyclic Transformers","date":"2024-05-06","arxiv_id":"2405.03429","n_code_links":1,"syntology":null},{"paper":"/paper/repvgg-gelan-enhanced-gelan-with-vgg-style","slug":"repvgg-gelan-enhanced-gelan-with-vgg-style","title":"RepVGG-GELAN: Enhanced GELAN with VGG-STYLE ConvNets for Brain Tumour Detection","date":"2024-05-06","arxiv_id":"2405.03541","n_code_links":1,"syntology":null},{"paper":"/paper/salient-object-detection-from-arbitrary","slug":"salient-object-detection-from-arbitrary","title":"Salient Object Detection From Arbitrary Modalities","date":"2024-05-06","arxiv_id":"2405.03352","n_code_links":1,"syntology":null},{"paper":null,"slug":"socialformer-social-interaction-modeling-with","title":"SocialFormer: Social Interaction Modeling with Edge-enhanced Heterogeneous Graph Transformers for Trajectory Prediction","date":"2024-05-06","arxiv_id":"2405.03809","n_code_links":0,"syntology":null},{"paper":"/paper/transformer-based-rgb-t-tracking-with-channel","slug":"transformer-based-rgb-t-tracking-with-channel","title":"Transformer-based RGB-T Tracking with Channel and Spatial Feature Fusion","date":"2024-05-06","arxiv_id":"2405.03177","n_code_links":1,"syntology":null},{"paper":null,"slug":"transformer-models-classify-random-numbers","title":"Transformer models as an efficient replacement for statistical test suites to evaluate the quality of random numbers","date":"2024-05-06","arxiv_id":"2405.03904","n_code_links":0,"syntology":null},{"paper":null,"slug":"can-large-language-models-make-the-grade-an","title":"Can Large Language Models Make the Grade? An Empirical Study Evaluating LLMs Ability to Mark Short Answer Questions in K-12 Education","date":"2024-05-05","arxiv_id":"2405.02985","n_code_links":0,"syntology":null},{"paper":null,"slug":"e-tsl-a-continuous-educational-turkish-sign","title":"E-TSL: A Continuous Educational Turkish Sign Language Dataset with Baseline Methods","date":"2024-05-05","arxiv_id":"2405.02984","n_code_links":0,"syntology":null},{"paper":"/paper/graph-as-point-set","slug":"graph-as-point-set","title":"Graph as Point Set","date":"2024-05-05","arxiv_id":"2405.02795","n_code_links":1,"syntology":{"ran":20,"of":27,"n_ran_checked":16,"n_instrument":4,"unverified":7,"pointer_only":27,"phrase":"20 ran (of which 15 constructed an object rather than computing a result; 16 with no instrument failure: 1 honoured, 0 violated, 15 with no contract checked; 4 where Syntology's instrument failed) · 7 unverified","official":{"repos":["GraphPKU/GraphAsSet"],"state":"official (archive's flag): 20 ran","n_ran":20,"n_constructed":15,"n_ran_no_instrument_failure":16,"n_unverified":7,"ran_from_kinds":["official"]}}},{"paper":"/paper/huixiangdou-cr-coreference-resolution-in","slug":"huixiangdou-cr-coreference-resolution-in","title":"Labeling supervised fine-tuning data with the scaling law","date":"2024-05-05","arxiv_id":"2405.02817","n_code_links":2,"syntology":null},{"paper":null,"slug":"iceformer-accelerated-inference-with-long","title":"IceFormer: Accelerated Inference with Long-Sequence Transformers on CPUs","date":"2024-05-05","arxiv_id":"2405.02842","n_code_links":0,"syntology":null},{"paper":null,"slug":"leveraging-lecture-content-for-improved","title":"Leveraging Lecture Content for Improved Feedback: Explorations with GPT-4 and Retrieval Augmented Generation","date":"2024-05-05","arxiv_id":"2405.06681","n_code_links":0,"syntology":null},{"paper":null,"slug":"multi-hop-graph-transformer-network-for-3d","title":"Multi-hop graph transformer network for 3D human pose estimation","date":"2024-05-05","arxiv_id":"2405.03055","n_code_links":0,"syntology":null},{"paper":"/paper/negativeprompt-leveraging-psychology-for","slug":"negativeprompt-leveraging-psychology-for","title":"NegativePrompt: Leveraging Psychology for Large Language Models Enhancement via Negative Emotional Stimuli","date":"2024-05-05","arxiv_id":"2405.02814","n_code_links":1,"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: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","official":{"repos":["wangxu0820/negativeprompt"],"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":"overconfidence-is-key-verbalized-uncertainty","title":"Overconfidence is Key: Verbalized Uncertainty Evaluation in Large Language and Vision-Language Models","date":"2024-05-05","arxiv_id":"2405.02917","n_code_links":0,"syntology":null},{"paper":null,"slug":"stochastic-rag-end-to-end-retrieval-augmented","title":"Stochastic RAG: End-to-End Retrieval-Augmented Generation through Expected Utility Maximization","date":"2024-05-05","arxiv_id":"2405.02816","n_code_links":0,"syntology":null},{"paper":"/paper/unraveling-the-dominance-of-large-language","slug":"unraveling-the-dominance-of-large-language","title":"Unraveling the Dominance of Large Language Models Over Transformer Models for Bangla Natural Language Inference: A Comprehensive Study","date":"2024-05-05","arxiv_id":"2405.02937","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-combination-of-bert-and-transformer-for","title":"A Combination of BERT and Transformer for Vietnamese Spelling Correction","date":"2024-05-04","arxiv_id":"2405.02573","n_code_links":0,"syntology":null},{"paper":null,"slug":"assessing-adversarial-robustness-of-large","title":"Assessing Adversarial Robustness of Large Language Models: An Empirical Study","date":"2024-05-04","arxiv_id":"2405.02764","n_code_links":0,"syntology":null},{"paper":null,"slug":"boosting-3d-neuron-segmentation-with-2d","title":"Boosting 3D Neuron Segmentation with 2D Vision Transformer Pre-trained on Natural Images","date":"2024-05-04","arxiv_id":"2405.02686","n_code_links":0,"syntology":null},{"paper":null,"slug":"open-sql-framework-enhancing-text-to-sql-on","title":"Open-SQL Framework: Enhancing Text-to-SQL on Open-source Large Language Models","date":"2024-05-04","arxiv_id":"2405.06674","n_code_links":0,"syntology":null},{"paper":"/paper/propertygpt-llm-driven-formal-verification-of","slug":"propertygpt-llm-driven-formal-verification-of","title":"PropertyGPT: LLM-driven Formal Verification of Smart Contracts through Retrieval-Augmented Property Generation","date":"2024-05-04","arxiv_id":"2405.02580","n_code_links":1,"syntology":null},{"paper":null,"slug":"vitals-vision-transformer-for-action","title":"ViTALS: Vision Transformer for Action Localization in Surgical Nephrectomy","date":"2024-05-04","arxiv_id":"2405.02571","n_code_links":0,"syntology":null},{"paper":"/paper/an-attention-based-pipeline-for-identifying","slug":"an-attention-based-pipeline-for-identifying","title":"An Attention Based Pipeline for Identifying Pre-Cancer Lesions in Head and Neck Clinical Images","date":"2024-05-03","arxiv_id":"2405.01937","n_code_links":1,"syntology":null},{"paper":null,"slug":"analyzing-narrative-processing-in-large","title":"Analyzing Narrative Processing in Large Language Models (LLMs): Using GPT4 to test BERT","date":"2024-05-03","arxiv_id":"2405.02024","n_code_links":0,"syntology":null},{"paper":"/paper/automating-the-enterprise-with-foundation","slug":"automating-the-enterprise-with-foundation","title":"Automating the Enterprise with Foundation Models","date":"2024-05-03","arxiv_id":"2405.03710","n_code_links":1,"syntology":{"ran":4,"of":5,"n_ran_checked":4,"n_instrument":0,"unverified":1,"pointer_only":1,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","official":{"repos":["hazyresearch/eclair-agents"],"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":"comparative-analysis-of-retrieval-systems-in","title":"Comparative Analysis of Retrieval Systems in the Real World","date":"2024-05-03","arxiv_id":"2405.02048","n_code_links":0,"syntology":null},{"paper":"/paper/cvtgad-simplified-transformer-with-cross-view","slug":"cvtgad-simplified-transformer-with-cross-view","title":"CVTGAD: Simplified Transformer with Cross-View Attention for Unsupervised Graph-level Anomaly Detection","date":"2024-05-03","arxiv_id":"2405.02359","n_code_links":1,"syntology":{"ran":2,"of":2,"n_ran_checked":2,"n_instrument":0,"unverified":0,"pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["jindongli-ai/cvtgad"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/dallmi-domain-adaption-for-llm-based-multi","slug":"dallmi-domain-adaption-for-llm-based-multi","title":"DALLMi: Domain Adaption for LLM-based Multi-label Classifier","date":"2024-05-03","arxiv_id":"2405.01883","n_code_links":1,"syntology":null},{"paper":null,"slug":"evaluating-large-language-models-for-2","title":"Evaluating Large Language Models for Structured Science Summarization in the Open Research Knowledge Graph","date":"2024-05-03","arxiv_id":"2405.02105","n_code_links":0,"syntology":null},{"paper":"/paper/exploiting-chatgpt-for-diagnosing-autism","slug":"exploiting-chatgpt-for-diagnosing-autism","title":"Exploiting ChatGPT for Diagnosing Autism-Associated Language Disorders and Identifying Distinct Features","date":"2024-05-03","arxiv_id":"2405.01799","n_code_links":1,"syntology":null},{"paper":null,"slug":"exploring-combinatorial-problem-solving-with","title":"Exploring Combinatorial Problem Solving with Large Language Models: A Case Study on the Travelling Salesman Problem Using GPT-3.5 Turbo","date":"2024-05-03","arxiv_id":"2405.01997","n_code_links":0,"syntology":null},{"paper":null,"slug":"federated-learning-for-tabular-data-using","title":"Federated Learning for Tabular Data using TabNet: A Vehicular Use-Case","date":"2024-05-03","arxiv_id":"2405.02060","n_code_links":0,"syntology":null},{"paper":null,"slug":"ifnet-deep-imaging-and-focusing-for-handheld","title":"IFNet: Deep Imaging and Focusing for Handheld SAR with Millimeter-wave Signals","date":"2024-05-03","arxiv_id":"2405.02023","n_code_links":0,"syntology":null},{"paper":null,"slug":"multi-method-integration-with-confidence","title":"Multi-method Integration with Confidence-based Weighting for Zero-shot Image Classification","date":"2024-05-03","arxiv_id":"2405.02155","n_code_links":0,"syntology":null},{"paper":null,"slug":"reasons-a-benchmark-for-retrieval-and","title":"Attribution in Scientific Literature: New Benchmark and Methods","date":"2024-05-03","arxiv_id":"2405.02228","n_code_links":0,"syntology":null},{"paper":"/paper/single-and-multi-hop-question-answering","slug":"single-and-multi-hop-question-answering","title":"Single and Multi-Hop Question-Answering Datasets for Reticular Chemistry with GPT-4-Turbo","date":"2024-05-03","arxiv_id":"2405.02128","n_code_links":1,"syntology":null},{"paper":null,"slug":"spatio-temporal-swinmae-a-swin-transformer","title":"SatSwinMAE: Efficient Autoencoding for Multiscale Time-series Satellite Imagery","date":"2024-05-03","arxiv_id":"2405.02512","n_code_links":0,"syntology":null},{"paper":"/paper/structural-pruning-of-pre-trained-language","slug":"structural-pruning-of-pre-trained-language","title":"Structural Pruning of Pre-trained Language Models via Neural Architecture Search","date":"2024-05-03","arxiv_id":"2405.02267","n_code_links":1,"syntology":null},{"paper":null,"slug":"technical-report-on-target-classification-in","title":"Technical report on target classification in SAR track","date":"2024-05-03","arxiv_id":"2405.02361","n_code_links":0,"syntology":null},{"paper":"/paper/a-survey-on-large-language-models-for-3","slug":"a-survey-on-large-language-models-for-3","title":"A Survey on Large Language Models for Critical Societal Domains: Finance, Healthcare, and Law","date":"2024-05-02","arxiv_id":"2405.01769","n_code_links":1,"syntology":null},{"paper":null,"slug":"bayesian-optimization-with-llm-based","title":"Bayesian Optimization with LLM-Based Acquisition Functions for Natural Language Preference Elicitation","date":"2024-05-02","arxiv_id":"2405.00981","n_code_links":0,"syntology":null},{"paper":null,"slug":"crossmpt-cross-attention-message-passing","title":"CrossMPT: Cross-attention Message-Passing Transformer for Error Correcting Codes","date":"2024-05-02","arxiv_id":"2405.01033","n_code_links":0,"syntology":null},{"paper":null,"slug":"domain-transferred-synthetic-data-generation","title":"Domain-Transferred Synthetic Data Generation for Improving Monocular Depth Estimation","date":"2024-05-02","arxiv_id":"2405.01113","n_code_links":0,"syntology":null},{"paper":null,"slug":"early-transformers-a-study-on-efficient","title":"Early Transformers: A study on Efficient Training of Transformer Models through Early-Bird Lottery Tickets","date":"2024-05-02","arxiv_id":"2405.02353","n_code_links":0,"syntology":null},{"paper":null,"slug":"how-can-i-get-it-right-using-gpt-to-rephrase","title":"How Can I Get It Right? Using GPT to Rephrase Incorrect Trainee Responses","date":"2024-05-02","arxiv_id":"2405.00970","n_code_links":0,"syntology":null},{"paper":null,"slug":"image-segmentation-of-treated-and-untreated","title":"Image segmentation of treated and untreated tumor spheroids by Fully Convolutional Networks","date":"2024-05-02","arxiv_id":"2405.01105","n_code_links":0,"syntology":null}],"record_sha256":"159a6ce4b6aaac412310210c0e6d35ecb4047e8e1baee37bb28ef3e14ab17df7","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}