{"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/adam/papers/76","list_of":"/method/adam","method":"Adam","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":76,"pages_in_order":244,"rows_per_page":100,"rows":[7501,7600],"of":24390,"counts":{"archive_papers_tagged":24390,"with_a_code_link":10944,"where_syntology_ran_a_sample":3424,"not_listed_spam_title":0,"listed":24390,"listed_where_code_ran":3424,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":2899,"every_run_a_failure_of_syntologys_instrument":525,"listed_with_a_run_with_no_instrument_failure":2899,"listed_every_run_a_failure_of_syntologys_instrument":525,"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/adam","prev":"/method/adam/papers/75","next":"/method/adam/papers/77","papers":[{"paper":"/paper/explainable-deep-learning-a-visual-analytics","slug":"explainable-deep-learning-a-visual-analytics","title":"Explainable Deep Learning: A Visual Analytics Approach with Transition Matrices","date":"2024-03-29","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/latxa-an-open-language-model-and-evaluation","slug":"latxa-an-open-language-model-and-evaluation","title":"Latxa: An Open Language Model and Evaluation Suite for Basque","date":"2024-03-29","arxiv_id":"2403.20266","n_code_links":1,"syntology":null},{"paper":null,"slug":"layernorm-a-key-component-in-parameter","title":"LayerNorm: A key component in parameter-efficient fine-tuning","date":"2024-03-29","arxiv_id":"2403.20284","n_code_links":0,"syntology":null},{"paper":"/paper/localising-the-seizure-onset-zone-from-single","slug":"localising-the-seizure-onset-zone-from-single","title":"Localising the Seizure Onset Zone from Single-Pulse Electrical Stimulation Responses with a CNN Transformer","date":"2024-03-29","arxiv_id":"2403.20324","n_code_links":1,"syntology":null},{"paper":"/paper/mango-a-benchmark-for-evaluating-mapping-and","slug":"mango-a-benchmark-for-evaluating-mapping-and","title":"MANGO: A Benchmark for Evaluating Mapping and Navigation Abilities of Large Language Models","date":"2024-03-29","arxiv_id":"2403.19913","n_code_links":1,"syntology":{"ran":1,"of":1,"n_ran_checked":0,"n_instrument":1,"unverified":0,"pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["oaklight/mango"],"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/on-the-fly-definition-augmentation-of-llms","slug":"on-the-fly-definition-augmentation-of-llms","title":"On-the-fly Definition Augmentation of LLMs for Biomedical NER","date":"2024-03-29","arxiv_id":"2404.00152","n_code_links":1,"syntology":{"ran":4,"of":7,"n_ran_checked":4,"n_instrument":0,"unverified":3,"pointer_only":0,"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) · 3 unverified","official":{"repos":["allenai/beacon"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":3,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"realm-reference-resolution-as-language","title":"ReALM: Reference Resolution As Language Modeling","date":"2024-03-29","arxiv_id":"2403.20329","n_code_links":0,"syntology":null},{"paper":"/paper/scenetracker-long-term-scene-flow-estimation","slug":"scenetracker-long-term-scene-flow-estimation","title":"SceneTracker: Long-term Scene Flow Estimation Network","date":"2024-03-29","arxiv_id":"2403.19924","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":["wwsource/scenetracker"],"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/shallow-cross-encoders-for-low-latency","slug":"shallow-cross-encoders-for-low-latency","title":"Shallow Cross-Encoders for Low-Latency Retrieval","date":"2024-03-29","arxiv_id":"2403.20222","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-novel-stochastic-transformer-based-approach","title":"A Novel Stochastic Transformer-based Approach for Post-Traumatic Stress Disorder Detection using Audio Recording of Clinical Interviews","date":"2024-03-28","arxiv_id":"2403.19441","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-review-of-multi-modal-large-language-and","title":"A Review of Multi-Modal Large Language and Vision Models","date":"2024-03-28","arxiv_id":"2404.01322","n_code_links":0,"syntology":null},{"paper":"/paper/aapmt-agi-assessment-through-prompt-and","slug":"aapmt-agi-assessment-through-prompt-and","title":"AAPMT: AGI Assessment Through Prompt and Metric Transformer","date":"2024-03-28","arxiv_id":"2403.19101","n_code_links":1,"syntology":null},{"paper":null,"slug":"alloybert-alloy-property-prediction-with","title":"AlloyBERT: Alloy Property Prediction with Large Language Models","date":"2024-03-28","arxiv_id":"2403.19783","n_code_links":0,"syntology":null},{"paper":"/paper/are-large-language-models-good-at-utility","slug":"are-large-language-models-good-at-utility","title":"Are Large Language Models Good at Utility Judgments?","date":"2024-03-28","arxiv_id":"2403.19216","n_code_links":1,"syntology":{"ran":3,"of":7,"n_ran_checked":3,"n_instrument":0,"unverified":4,"pointer_only":7,"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) · 4 unverified","official":{"repos":["ict-bigdatalab/utility_judgments"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":4,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"checkpoint-merging-via-bayesian-optimization","title":"Checkpoint Merging via Bayesian Optimization in LLM Pretraining","date":"2024-03-28","arxiv_id":"2403.19390","n_code_links":0,"syntology":null},{"paper":null,"slug":"code-comparison-tuning-for-code-large","title":"Code Comparison Tuning for Code Large Language Models","date":"2024-03-28","arxiv_id":"2403.19121","n_code_links":0,"syntology":null},{"paper":"/paper/densenets-reloaded-paradigm-shift-beyond","slug":"densenets-reloaded-paradigm-shift-beyond","title":"DenseNets Reloaded: Paradigm Shift Beyond ResNets and ViTs","date":"2024-03-28","arxiv_id":"2403.19588","n_code_links":3,"syntology":{"ran":3,"of":3,"n_ran_checked":3,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 3 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["huggingface/pytorch-image-models","naver-ai/rdnet"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"enhancing-efficiency-in-vision-transformer","title":"Enhancing Efficiency in Vision Transformer Networks: Design Techniques and Insights","date":"2024-03-28","arxiv_id":"2403.19882","n_code_links":0,"syntology":null},{"paper":null,"slug":"factoid-factual-entailment-for-hallucination","title":"FACTOID: FACtual enTailment fOr hallucInation Detection","date":"2024-03-28","arxiv_id":"2403.19113","n_code_links":0,"syntology":null},{"paper":null,"slug":"generate-then-retrieve-conversational","title":"Generating Multi-Aspect Queries for Conversational Search","date":"2024-03-28","arxiv_id":"2403.19302","n_code_links":0,"syntology":null},{"paper":"/paper/genetic-quantization-aware-approximation-for","slug":"genetic-quantization-aware-approximation-for","title":"Genetic Quantization-Aware Approximation for Non-Linear Operations in Transformers","date":"2024-03-28","arxiv_id":"2403.19591","n_code_links":1,"syntology":null},{"paper":null,"slug":"intelligent-classification-and-personalized","title":"Intelligent Classification and Personalized Recommendation of E-commerce Products Based on Machine Learning","date":"2024-03-28","arxiv_id":"2403.19345","n_code_links":0,"syntology":null},{"paper":"/paper/interpreting-key-mechanisms-of-factual-recall","slug":"interpreting-key-mechanisms-of-factual-recall","title":"Interpreting Key Mechanisms of Factual Recall in Transformer-Based Language Models","date":"2024-03-28","arxiv_id":"2403.19521","n_code_links":1,"syntology":{"ran":12,"of":14,"n_ran_checked":12,"n_instrument":0,"unverified":2,"pointer_only":14,"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) · 2 unverified","official":{"repos":["trestad/factual-recall-mechanism"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":12,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":"/paper/jamba-a-hybrid-transformer-mamba-language","slug":"jamba-a-hybrid-transformer-mamba-language","title":"Jamba: A Hybrid Transformer-Mamba Language Model","date":"2024-03-28","arxiv_id":"2403.19887","n_code_links":3,"syntology":null},{"paper":null,"slug":"just-dna-seq-open-source-personal-genomics","title":"Just-DNA-Seq, open-source personal genomics platform: longevity science for everyone","date":"2024-03-28","arxiv_id":"2403.19087","n_code_links":0,"syntology":null},{"paper":null,"slug":"keypoint-action-tokens-enable-in-context","title":"Keypoint Action Tokens Enable In-Context Imitation Learning in Robotics","date":"2024-03-28","arxiv_id":"2403.19578","n_code_links":0,"syntology":null},{"paper":"/paper/mateval-a-multi-agent-discussion-framework","slug":"mateval-a-multi-agent-discussion-framework","title":"MATEval: A Multi-Agent Discussion Framework for Advancing Open-Ended Text Evaluation","date":"2024-03-28","arxiv_id":"2403.19305","n_code_links":1,"syntology":null},{"paper":null,"slug":"risk-prediction-of-pathological-gambling-on","title":"Risk prediction of pathological gambling on social media","date":"2024-03-28","arxiv_id":"2403.19358","n_code_links":0,"syntology":null},{"paper":null,"slug":"single-shared-network-with-prior-inspired","title":"Single-Shared Network with Prior-Inspired Loss for Parameter-Efficient Multi-Modal Imaging Skin Lesion Classification","date":"2024-03-28","arxiv_id":"2403.19203","n_code_links":0,"syntology":null},{"paper":"/paper/a-novel-corpus-of-annotated-medical-imaging","slug":"a-novel-corpus-of-annotated-medical-imaging","title":"A Novel Corpus of Annotated Medical Imaging Reports and Information Extraction Results Using BERT-based Language Models","date":"2024-03-27","arxiv_id":"2403.18975","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-survey-on-large-language-models-from","title":"A Survey on Large Language Models from Concept to Implementation","date":"2024-03-27","arxiv_id":"2403.18969","n_code_links":0,"syntology":null},{"paper":null,"slug":"acted-automatic-acquisition-of-typical-event","title":"AcTED: Automatic Acquisition of Typical Event Duration for Semi-supervised Temporal Commonsense QA","date":"2024-03-27","arxiv_id":"2403.18504","n_code_links":0,"syntology":null},{"paper":null,"slug":"attention-aware-semantic-relevance-predicting","title":"Attention-aware semantic relevance predicting Chinese sentence reading","date":"2024-03-27","arxiv_id":"2403.18542","n_code_links":0,"syntology":null},{"paper":"/paper/biomedlm-a-2-7b-parameter-language-model","slug":"biomedlm-a-2-7b-parameter-language-model","title":"BioMedLM: A 2.7B Parameter Language Model Trained On Biomedical Text","date":"2024-03-27","arxiv_id":"2403.18421","n_code_links":1,"syntology":{"ran":3,"of":4,"n_ran_checked":0,"n_instrument":3,"unverified":1,"pointer_only":4,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 1 unverified","official":{"repos":["stanford-crfm/biomedlm"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"blade-enhancing-black-box-large-language","title":"BLADE: Enhancing Black-box Large Language Models with Small Domain-Specific Models","date":"2024-03-27","arxiv_id":"2403.18365","n_code_links":0,"syntology":null},{"paper":null,"slug":"boosting-conversational-question-answering","title":"Boosting Conversational Question Answering with Fine-Grained Retrieval-Augmentation and Self-Check","date":"2024-03-27","arxiv_id":"2403.18243","n_code_links":0,"syntology":null},{"paper":null,"slug":"cpr-retrieval-augmented-generation-for","title":"CPR: Retrieval Augmented Generation for Copyright Protection","date":"2024-03-27","arxiv_id":"2403.18920","n_code_links":0,"syntology":null},{"paper":null,"slug":"cross-domain-fiber-cluster-shape-analysis-for","title":"Cross-domain Fiber Cluster Shape Analysis for Language Performance Cognitive Score Prediction","date":"2024-03-27","arxiv_id":"2403.19001","n_code_links":0,"syntology":null},{"paper":null,"slug":"evaluating-large-language-models-for-health-1","title":"Evaluating Large Language Models for Health-Related Text Classification Tasks with Public Social Media Data","date":"2024-03-27","arxiv_id":"2403.19031","n_code_links":0,"syntology":null},{"paper":"/paper/faster-convergence-for-transformer-fine","slug":"faster-convergence-for-transformer-fine","title":"Faster Convergence for Transformer Fine-tuning with Line Search Methods","date":"2024-03-27","arxiv_id":"2403.18506","n_code_links":1,"syntology":null},{"paper":null,"slug":"few-shot-cross-system-anomaly-trace","title":"Cross-System Categorization of Abnormal Traces in Microservice-Based Systems via Meta-Learning","date":"2024-03-27","arxiv_id":"2403.18998","n_code_links":0,"syntology":null},{"paper":null,"slug":"fourier-or-wavelet-bases-as-counterpart-self","title":"Fourier or Wavelet bases as counterpart self-attention in spikformer for efficient visual classification","date":"2024-03-27","arxiv_id":"2403.18228","n_code_links":0,"syntology":null},{"paper":null,"slug":"fusion-approaches-for-emotion-recognition","title":"Fusion approaches for emotion recognition from speech using acoustic and text-based features","date":"2024-03-27","arxiv_id":"2403.18635","n_code_links":0,"syntology":null},{"paper":null,"slug":"illicit-object-detection-in-x-ray-images","title":"Illicit object detection in X-ray images using Vision Transformers","date":"2024-03-27","arxiv_id":"2403.19043","n_code_links":0,"syntology":null},{"paper":"/paper/improving-line-search-methods-for-large-scale","slug":"improving-line-search-methods-for-large-scale","title":"Improving Line Search Methods for Large Scale Neural Network Training","date":"2024-03-27","arxiv_id":"2403.18519","n_code_links":1,"syntology":null},{"paper":null,"slug":"learning-in-pinns-phase-transition-total","title":"Learning in PINNs: Phase transition, total diffusion, and generalization","date":"2024-03-27","arxiv_id":"2403.18494","n_code_links":0,"syntology":null},{"paper":null,"slug":"llms-in-hci-data-work-bridging-the-gap","title":"LLMs in HCI Data Work: Bridging the Gap Between Information Retrieval and Responsible Research Practices","date":"2024-03-27","arxiv_id":"2403.18173","n_code_links":0,"syntology":null},{"paper":"/paper/long-form-factuality-in-large-language-models","slug":"long-form-factuality-in-large-language-models","title":"Long-form factuality in large language models","date":"2024-03-27","arxiv_id":"2403.18802","n_code_links":3,"syntology":{"ran":5,"of":5,"n_ran_checked":5,"n_instrument":0,"unverified":0,"pointer_only":4,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["google-deepmind/long-form-factuality"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"paper":null,"slug":"malbert-is-a-compact-multilingual-bert-model","title":"mALBERT: Is a Compact Multilingual BERT Model Still Worth It?","date":"2024-03-27","arxiv_id":"2403.18338","n_code_links":0,"syntology":null},{"paper":"/paper/mini-gemini-mining-the-potential-of-multi","slug":"mini-gemini-mining-the-potential-of-multi","title":"Mini-Gemini: Mining the Potential of Multi-modality Vision Language Models","date":"2024-03-27","arxiv_id":"2403.18814","n_code_links":2,"syntology":{"ran":8,"of":8,"n_ran_checked":5,"n_instrument":3,"unverified":0,"pointer_only":1,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 1 honoured, 1 violated, 3 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","official":{"repos":["dvlab-research/minigemini"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/parco-part-coordinating-text-to-motion","slug":"parco-part-coordinating-text-to-motion","title":"ParCo: Part-Coordinating Text-to-Motion Synthesis","date":"2024-03-27","arxiv_id":"2403.18512","n_code_links":1,"syntology":null},{"paper":"/paper/rankmamba-benchmarking-mamba-s-document","slug":"rankmamba-benchmarking-mamba-s-document","title":"RankMamba: Benchmarking Mamba's Document Ranking Performance in the Era of Transformers","date":"2024-03-27","arxiv_id":"2403.18276","n_code_links":1,"syntology":null},{"paper":"/paper/reshaping-free-text-radiology-notes-into","slug":"reshaping-free-text-radiology-notes-into","title":"Reshaping Free-Text Radiology Notes Into Structured Reports With Generative Transformers","date":"2024-03-27","arxiv_id":"2403.18938","n_code_links":1,"syntology":null},{"paper":"/paper/semrode-macro-adversarial-training-to-learn","slug":"semrode-macro-adversarial-training-to-learn","title":"SemRoDe: Macro Adversarial Training to Learn Representations That are Robust to Word-Level Attacks","date":"2024-03-27","arxiv_id":"2403.18423","n_code_links":1,"syntology":{"ran":1,"of":3,"n_ran_checked":1,"n_instrument":0,"unverified":2,"pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","official":{"repos":["aniloid2/semrode-macroadversarialtraining"],"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":"vitar-vision-transformer-with-any-resolution","title":"ViTAR: Vision Transformer with Any Resolution","date":"2024-03-27","arxiv_id":"2403.18361","n_code_links":0,"syntology":null},{"paper":"/paper/vulnerability-detection-with-code-language","slug":"vulnerability-detection-with-code-language","title":"Vulnerability Detection with Code Language Models: How Far Are We?","date":"2024-03-27","arxiv_id":"2403.18624","n_code_links":1,"syntology":{"ran":6,"of":10,"n_ran_checked":6,"n_instrument":0,"unverified":4,"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) · 4 unverified","official":{"repos":["dlvuldet/primevul"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":4,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"alisa-accelerating-large-language-model","title":"ALISA: Accelerating Large Language Model Inference via Sparsity-Aware KV Caching","date":"2024-03-26","arxiv_id":"2403.17312","n_code_links":0,"syntology":null},{"paper":"/paper/are-compressed-language-models-less-subgroup","slug":"are-compressed-language-models-less-subgroup","title":"Are Compressed Language Models Less Subgroup Robust?","date":"2024-03-26","arxiv_id":"2403.17811","n_code_links":1,"syntology":null},{"paper":null,"slug":"automated-report-generation-for-lung","title":"Automated Report Generation for Lung Cytological Images Using a CNN Vision Classifier and Multiple-Transformer Text Decoders: Preliminary Study","date":"2024-03-26","arxiv_id":"2403.18151","n_code_links":0,"syntology":null},{"paper":null,"slug":"ccdsreformer-traffic-flow-prediction-with-a","title":"CCDSReFormer: Traffic Flow Prediction with a Criss-Crossed Dual-Stream Enhanced Rectified Transformer Model","date":"2024-03-26","arxiv_id":"2403.17753","n_code_links":0,"syntology":null},{"paper":"/paper/constructions-are-so-difficult-that-even","slug":"constructions-are-so-difficult-that-even","title":"Constructions Are So Difficult That Even Large Language Models Get Them Right for the Wrong Reasons","date":"2024-03-26","arxiv_id":"2403.17760","n_code_links":1,"syntology":null},{"paper":null,"slug":"decoding-probing-revealing-internal","title":"Decoding Probing: Revealing Internal Linguistic Structures in Neural Language Models using Minimal Pairs","date":"2024-03-26","arxiv_id":"2403.17299","n_code_links":0,"syntology":null},{"paper":null,"slug":"disambiguate-entity-matching-through-relation","title":"Disambiguate Entity Matching using Large Language Models through Relation Discovery","date":"2024-03-26","arxiv_id":"2403.17344","n_code_links":0,"syntology":null},{"paper":"/paper/don-t-trust-verify-grounding-llm-quantitative","slug":"don-t-trust-verify-grounding-llm-quantitative","title":"Don't Trust: Verify -- Grounding LLM Quantitative Reasoning with Autoformalization","date":"2024-03-26","arxiv_id":"2403.18120","n_code_links":1,"syntology":{"ran":10,"of":12,"n_ran_checked":2,"n_instrument":8,"unverified":2,"pointer_only":0,"phrase":"10 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; 8 where Syntology's instrument failed) · 2 unverified","official":{"repos":["jinpz/dtv"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":"/paper/egoposeformer-a-simple-baseline-for","slug":"egoposeformer-a-simple-baseline-for","title":"EgoPoseFormer: A Simple Baseline for Stereo Egocentric 3D Human Pose Estimation","date":"2024-03-26","arxiv_id":"2403.18080","n_code_links":1,"syntology":null},{"paper":"/paper/ellen-extremely-lightly-supervised-learning","slug":"ellen-extremely-lightly-supervised-learning","title":"ELLEN: Extremely Lightly Supervised Learning For Efficient Named Entity Recognition","date":"2024-03-26","arxiv_id":"2403.17385","n_code_links":1,"syntology":null},{"paper":null,"slug":"enhancing-legal-document-retrieval-a-multi","title":"Enhancing Legal Document Retrieval: A Multi-Phase Approach with Large Language Models","date":"2024-03-26","arxiv_id":"2403.18093","n_code_links":0,"syntology":null},{"paper":null,"slug":"evaluating-the-efficacy-of-prompt-engineered","title":"Evaluating the Efficacy of Prompt-Engineered Large Multimodal Models Versus Fine-Tuned Vision Transformers in Image-Based Security Applications","date":"2024-03-26","arxiv_id":"2403.17787","n_code_links":0,"syntology":null},{"paper":"/paper/fingerprinting-web-servers-through","slug":"fingerprinting-web-servers-through","title":"Fingerprinting web servers through Transformer-encoded HTTP response headers","date":"2024-03-26","arxiv_id":"2404.00056","n_code_links":1,"syntology":null},{"paper":"/paper/hierarchical-light-transformer-ensembles-for","slug":"hierarchical-light-transformer-ensembles-for","title":"Hierarchical Light Transformer Ensembles for Multimodal Trajectory Forecasting","date":"2024-03-26","arxiv_id":"2403.17678","n_code_links":1,"syntology":null},{"paper":null,"slug":"hierarchical-multi-label-classification-for","title":"Hierarchical Multi-label Classification for Fine-level Event Extraction from Aviation Accident Reports","date":"2024-03-26","arxiv_id":"2403.17914","n_code_links":0,"syntology":null},{"paper":null,"slug":"integrative-graph-transformer-framework-for","title":"Integrative Graph-Transformer Framework for Histopathology Whole Slide Image Representation and Classification","date":"2024-03-26","arxiv_id":"2403.18134","n_code_links":0,"syntology":null},{"paper":"/paper/internlm2-technical-report","slug":"internlm2-technical-report","title":"InternLM2 Technical Report","date":"2024-03-26","arxiv_id":"2403.17297","n_code_links":3,"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":null}},{"paper":null,"slug":"large-language-models-are-state-of-the-art-2","title":"Large Language Models Are State-of-the-Art Evaluator for Grammatical Error Correction","date":"2024-03-26","arxiv_id":"2403.17540","n_code_links":0,"syntology":null},{"paper":null,"slug":"magis-llm-based-multi-agent-framework-for","title":"MAGIS: LLM-Based Multi-Agent Framework for GitHub Issue Resolution","date":"2024-03-26","arxiv_id":"2403.17927","n_code_links":0,"syntology":null},{"paper":"/paper/mechanistic-design-and-scaling-of-hybrid","slug":"mechanistic-design-and-scaling-of-hybrid","title":"Mechanistic Design and Scaling of Hybrid Architectures","date":"2024-03-26","arxiv_id":"2403.17844","n_code_links":1,"syntology":{"ran":4,"of":4,"n_ran_checked":3,"n_instrument":1,"unverified":0,"pointer_only":0,"phrase":"4 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; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["athms/mad-lab"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"not-all-similarities-are-created-equal","title":"Not All Similarities Are Created Equal: Leveraging Data-Driven Biases to Inform GenAI Copyright Disputes","date":"2024-03-26","arxiv_id":"2403.17691","n_code_links":0,"syntology":null},{"paper":"/paper/omnivid-a-generative-framework-for-universal","slug":"omnivid-a-generative-framework-for-universal","title":"OmniVid: A Generative Framework for Universal Video Understanding","date":"2024-03-26","arxiv_id":"2403.17935","n_code_links":1,"syntology":{"ran":7,"of":11,"n_ran_checked":6,"n_instrument":1,"unverified":4,"pointer_only":11,"phrase":"7 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; 1 where Syntology's instrument failed) · 4 unverified","official":{"repos":["wangjk666/omnivid"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":4,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"rotate-to-scan-unet-like-mamba-with-triplet","title":"Rotate to Scan: UNet-like Mamba with Triplet SSM Module for Medical Image Segmentation","date":"2024-03-26","arxiv_id":"2403.17701","n_code_links":0,"syntology":null},{"paper":"/paper/sghormer-an-energy-saving-graph-transformer","slug":"sghormer-an-energy-saving-graph-transformer","title":"SGHormer: An Energy-Saving Graph Transformer Driven by Spikes","date":"2024-03-26","arxiv_id":"2403.17656","n_code_links":1,"syntology":null},{"paper":"/paper/sledge-synthesizing-simulation-environments","slug":"sledge-synthesizing-simulation-environments","title":"SLEDGE: Synthesizing Driving Environments with Generative Models and Rule-Based Traffic","date":"2024-03-26","arxiv_id":"2403.17933","n_code_links":1,"syntology":null},{"paper":null,"slug":"supervisory-prompt-training","title":"Supervisory Prompt Training","date":"2024-03-26","arxiv_id":"2403.18051","n_code_links":0,"syntology":null},{"paper":"/paper/targeted-visualization-of-the-backbone-of","slug":"targeted-visualization-of-the-backbone-of","title":"Targeted Visualization of the Backbone of Encoder LLMs","date":"2024-03-26","arxiv_id":"2403.18872","n_code_links":1,"syntology":null},{"paper":null,"slug":"verbing-weirds-language-models-evaluation-of","title":"Verbing Weirds Language (Models): Evaluation of English Zero-Derivation in Five LLMs","date":"2024-03-26","arxiv_id":"2403.17856","n_code_links":0,"syntology":null},{"paper":"/paper/3d-effivitcaps-3d-efficient-vision","slug":"3d-effivitcaps-3d-efficient-vision","title":"3D-EffiViTCaps: 3D Efficient Vision Transformer with Capsule for Medical Image Segmentation","date":"2024-03-25","arxiv_id":"2403.16350","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-comparative-analysis-of-embedding-models","title":"A comparative analysis of embedding models for patent similarity","date":"2024-03-25","arxiv_id":"2403.16630","n_code_links":0,"syntology":null},{"paper":"/paper/a-comparison-of-human-gpt-3-5-and-gpt-4","slug":"a-comparison-of-human-gpt-3-5-and-gpt-4","title":"A comparison of Human, GPT-3.5, and GPT-4 Performance in a University-Level Coding Course","date":"2024-03-25","arxiv_id":"2403.16977","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-study-on-how-attention-scores-in-the-bert","title":"A Study on How Attention Scores in the BERT Model are Aware of Lexical Categories in Syntactic and Semantic Tasks on the GLUE Benchmark","date":"2024-03-25","arxiv_id":"2403.16447","n_code_links":0,"syntology":null},{"paper":"/paper/an-end-to-end-structure-with-novel-position","slug":"an-end-to-end-structure-with-novel-position","title":"An End-to-End Structure with Novel Position Mechanism and Improved EMD for Stock Forecasting","date":"2024-03-25","arxiv_id":"2404.07969","n_code_links":1,"syntology":null},{"paper":null,"slug":"chebmixer-efficient-graph-representation","title":"ChebMixer: Efficient Graph Representation Learning with MLP Mixer","date":"2024-03-25","arxiv_id":"2403.16358","n_code_links":0,"syntology":null},{"paper":"/paper/ct-bound-fast-boundary-estimation-from-noisy","slug":"ct-bound-fast-boundary-estimation-from-noisy","title":"CT-Bound: Robust Boundary Detection From Noisy Images Via Hybrid Convolution and Transformer Neural Networks","date":"2024-03-25","arxiv_id":"2403.16494","n_code_links":1,"syntology":null},{"paper":null,"slug":"cvt-xrf-contrastive-in-voxel-transformer-for","title":"CVT-xRF: Contrastive In-Voxel Transformer for 3D Consistent Radiance Fields from Sparse Inputs","date":"2024-03-25","arxiv_id":"2403.16885","n_code_links":0,"syntology":null},{"paper":null,"slug":"do-llm-agents-have-regret-a-case-study-in","title":"Do LLM Agents Have Regret? A Case Study in Online Learning and Games","date":"2024-03-25","arxiv_id":"2403.16843","n_code_links":0,"syntology":null},{"paper":"/paper/doctr-disentangled-object-centric-transformer","slug":"doctr-disentangled-object-centric-transformer","title":"DOCTR: Disentangled Object-Centric Transformer for Point Scene Understanding","date":"2024-03-25","arxiv_id":"2403.16431","n_code_links":1,"syntology":null},{"paper":null,"slug":"gpt-4-understands-discourse-at-least-as-well","title":"Text Understanding in GPT-4 vs Humans","date":"2024-03-25","arxiv_id":"2403.17196","n_code_links":0,"syntology":null},{"paper":null,"slug":"grammatical-vs-spelling-error-correction-an","title":"Grammatical vs Spelling Error Correction: An Investigation into the Responsiveness of Transformer-based Language Models using BART and MarianMT","date":"2024-03-25","arxiv_id":"2403.16655","n_code_links":0,"syntology":null},{"paper":"/paper/iterative-refinement-of-project-level-code","slug":"iterative-refinement-of-project-level-code","title":"Iterative Refinement of Project-Level Code Context for Precise Code Generation with Compiler Feedback","date":"2024-03-25","arxiv_id":"2403.16792","n_code_links":1,"syntology":null},{"paper":"/paper/linear-cross-document-event-coreference","slug":"linear-cross-document-event-coreference","title":"Linear Cross-document Event Coreference Resolution with X-AMR","date":"2024-03-25","arxiv_id":"2404.08656","n_code_links":1,"syntology":null},{"paper":"/paper/lsttn-a-long-short-term-transformer-based","slug":"lsttn-a-long-short-term-transformer-based","title":"LSTTN: A Long-Short Term Transformer-based Spatio-temporal Neural Network for Traffic Flow Forecasting","date":"2024-03-25","arxiv_id":"2403.16495","n_code_links":1,"syntology":null},{"paper":"/paper/modetv2-gpu-accelerated-motion-decomposition","slug":"modetv2-gpu-accelerated-motion-decomposition","title":"ModeTv2: GPU-accelerated Motion Decomposition Transformer for Pairwise Optimization in Medical Image Registration","date":"2024-03-25","arxiv_id":"2403.16526","n_code_links":2,"syntology":null}],"record_sha256":"07c33cc8426747fbc0098ba1f2d9b8add03040c1f4d96aa1a6fe1eb51b728c95","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}