{"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/119","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":119,"pages_in_order":255,"rows_per_page":100,"rows":[11801,11900],"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/118","next":"/method/linear-layer/papers/120","papers":[{"paper":null,"slug":"rgat-a-deeper-look-into-syntactic-dependency","title":"RGAT: A Deeper Look into Syntactic Dependency Information for Coreference Resolution","date":"2023-09-10","arxiv_id":"2309.04977","n_code_links":0,"syntology":null},{"paper":null,"slug":"sc-nerf-self-correcting-neural-radiance-field","title":"SC-NeRF: Self-Correcting Neural Radiance Field with Sparse Views","date":"2023-09-10","arxiv_id":"2309.05028","n_code_links":0,"syntology":null},{"paper":null,"slug":"unified-contrastive-fusion-transformer-for","title":"Unified Contrastive Fusion Transformer for Multimodal Human Action Recognition","date":"2023-09-10","arxiv_id":"2309.05032","n_code_links":0,"syntology":null},{"paper":null,"slug":"denoising-mot-towards-multiple-object","title":"DeNoising-MOT: Towards Multiple Object Tracking with Severe Occlusions","date":"2023-09-09","arxiv_id":"2309.04682","n_code_links":0,"syntology":null},{"paper":null,"slug":"efficient-finetuning-large-language-models","title":"Efficient Finetuning Large Language Models For Vietnamese Chatbot","date":"2023-09-09","arxiv_id":"2309.04646","n_code_links":0,"syntology":null},{"paper":"/paper/few-shot-medical-image-segmentation-via-a","slug":"few-shot-medical-image-segmentation-via-a","title":"Few-Shot Medical Image Segmentation via a Region-enhanced Prototypical Transformer","date":"2023-09-09","arxiv_id":"2309.04825","n_code_links":1,"syntology":null},{"paper":null,"slug":"how-to-evaluate-semantic-communications-for","title":"How to Evaluate Semantic Communications for Images with ViTScore Metric?","date":"2023-09-09","arxiv_id":"2309.04891","n_code_links":0,"syntology":null},{"paper":null,"slug":"self-supervised-transformer-with-domain","title":"Latent Spatiotemporal Adaptation for Generalized Face Forgery Video Detection","date":"2023-09-09","arxiv_id":"2309.04795","n_code_links":0,"syntology":null},{"paper":null,"slug":"transformer-based-deep-learning-detector-for","title":"Transformer-Based Deep Learning Detector for Dual-Mode Index Modulation 3D-OFDM","date":"2023-09-09","arxiv_id":"2309.04764","n_code_links":0,"syntology":null},{"paper":null,"slug":"can-nlp-models-identify-distinguish-and","title":"Can NLP Models 'Identify', 'Distinguish', and 'Justify' Questions that Don't have a Definitive Answer?","date":"2023-09-08","arxiv_id":"2309.04635","n_code_links":0,"syntology":null},{"paper":"/paper/cnn-injected-transformer-for-image-exposure","slug":"cnn-injected-transformer-for-image-exposure","title":"CNN Injected Transformer for Image Exposure Correction","date":"2023-09-08","arxiv_id":"2309.04366","n_code_links":1,"syntology":null},{"paper":null,"slug":"context-aware-prompt-tuning-for-vision","title":"Context-Aware Prompt Tuning for Vision-Language Model with Dual-Alignment","date":"2023-09-08","arxiv_id":"2309.04158","n_code_links":0,"syntology":null},{"paper":null,"slug":"curve-your-attention-mixed-curvature","title":"Curve Your Attention: Mixed-Curvature Transformers for Graph Representation Learning","date":"2023-09-08","arxiv_id":"2309.04082","n_code_links":0,"syntology":null},{"paper":"/paper/encoding-multi-domain-scientific-papers-by","slug":"encoding-multi-domain-scientific-papers-by","title":"Encoding Multi-Domain Scientific Papers by Ensembling Multiple CLS Tokens","date":"2023-09-08","arxiv_id":"2309.04333","n_code_links":1,"syntology":{"ran":0,"of":1,"n_ran_checked":0,"n_instrument":0,"unverified":1,"pointer_only":0,"phrase":"0 ran · 1 unverified","official":{"repos":["ronaldseoh/multi2spe"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"paper":"/paper/fimo-a-challenge-formal-dataset-for-automated","slug":"fimo-a-challenge-formal-dataset-for-automated","title":"FIMO: A Challenge Formal Dataset for Automated Theorem Proving","date":"2023-09-08","arxiv_id":"2309.04295","n_code_links":1,"syntology":null},{"paper":null,"slug":"from-sparse-to-dense-gpt-4-summarization-with","title":"From Sparse to Dense: GPT-4 Summarization with Chain of Density Prompting","date":"2023-09-08","arxiv_id":"2309.04269","n_code_links":0,"syntology":null},{"paper":"/paper/fuzzy-fingerprinting-transformer-language","slug":"fuzzy-fingerprinting-transformer-language","title":"Fuzzy Fingerprinting Transformer Language-Models for Emotion Recognition in Conversations","date":"2023-09-08","arxiv_id":"2309.04292","n_code_links":0,"syntology":null},{"paper":"/paper/language-prompt-for-autonomous-driving","slug":"language-prompt-for-autonomous-driving","title":"Language Prompt for Autonomous Driving","date":"2023-09-08","arxiv_id":"2309.04379","n_code_links":1,"syntology":{"ran":2,"of":2,"n_ran_checked":0,"n_instrument":2,"unverified":0,"pointer_only":1,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","official":{"repos":["wudongming97/prompt4driving"],"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","unlocated"]}}},{"paper":null,"slug":"leveraging-pretrained-image-text-models-for","title":"Leveraging Pretrained Image-text Models for Improving Audio-Visual Learning","date":"2023-09-08","arxiv_id":"2309.04628","n_code_links":0,"syntology":null},{"paper":"/paper/nestle-a-no-code-tool-for-statistical","slug":"nestle-a-no-code-tool-for-statistical","title":"NESTLE: a No-Code Tool for Statistical Analysis of Legal Corpus","date":"2023-09-08","arxiv_id":"2309.04146","n_code_links":1,"syntology":null},{"paper":"/paper/uq-at-smm4h-2023-alex-for-public-health","slug":"uq-at-smm4h-2023-alex-for-public-health","title":"UQ at #SMM4H 2023: ALEX for Public Health Analysis with Social Media","date":"2023-09-08","arxiv_id":"2309.04213","n_code_links":1,"syntology":null},{"paper":null,"slug":"adapting-self-supervised-representations-to","title":"Adapting Self-Supervised Representations to Multi-Domain Setups","date":"2023-09-07","arxiv_id":"2309.03999","n_code_links":0,"syntology":null},{"paper":null,"slug":"enhancing-pipeline-based-conversational","title":"Enhancing Pipeline-Based Conversational Agents with Large Language Models","date":"2023-09-07","arxiv_id":"2309.03748","n_code_links":0,"syntology":null},{"paper":"/paper/evaluating-chatgpt-as-a-recommender-system-a","slug":"evaluating-chatgpt-as-a-recommender-system-a","title":"Evaluating ChatGPT as a Recommender System: A Rigorous Approach","date":"2023-09-07","arxiv_id":"2309.03613","n_code_links":1,"syntology":null},{"paper":"/paper/evaluating-the-efficacy-of-supervised","slug":"evaluating-the-efficacy-of-supervised","title":"Supervised Learning and Large Language Model Benchmarks on Mental Health Datasets: Cognitive Distortions and Suicidal Risks in Chinese Social Media","date":"2023-09-07","arxiv_id":"2309.03564","n_code_links":2,"syntology":null},{"paper":"/paper/evaluation-of-large-language-models-for","slug":"evaluation-of-large-language-models-for","title":"Evaluation of large language models for discovery of gene set function","date":"2023-09-07","arxiv_id":"2309.04019","n_code_links":1,"syntology":null},{"paper":null,"slug":"flm-101b-an-open-llm-and-how-to-train-it-with","title":"FLM-101B: An Open LLM and How to Train It with $100K Budget","date":"2023-09-07","arxiv_id":"2309.03852","n_code_links":0,"syntology":null},{"paper":null,"slug":"ms-unet-v2-adaptive-denoising-method-and","title":"MS-UNet-v2: Adaptive Denoising Method and Training Strategy for Medical Image Segmentation with Small Training Data","date":"2023-09-07","arxiv_id":"2309.03686","n_code_links":0,"syntology":null},{"paper":"/paper/multimodal-transformer-for-material","slug":"multimodal-transformer-for-material","title":"MMSFormer: Multimodal Transformer for Material and Semantic Segmentation","date":"2023-09-07","arxiv_id":"2309.04001","n_code_links":1,"syntology":null},{"paper":"/paper/propainter-improving-propagation-and","slug":"propainter-improving-propagation-and","title":"ProPainter: Improving Propagation and Transformer for Video Inpainting","date":"2023-09-07","arxiv_id":"2309.03897","n_code_links":3,"syntology":{"ran":20,"of":34,"n_ran_checked":12,"n_instrument":8,"unverified":14,"pointer_only":16,"phrase":"20 ran (of which 10 constructed an object rather than computing a result; 12 with no instrument failure: 0 honoured, 0 violated, 12 with no contract checked; 8 where Syntology's instrument failed) · 14 unverified","official":{"repos":["sczhou/propainter"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":5,"n_ran_no_instrument_failure":6,"n_unverified":6,"ran_from_kinds":["listed","official"]}}},{"paper":"/paper/s-adapter-generalizing-vision-transformer-for","slug":"s-adapter-generalizing-vision-transformer-for","title":"S-Adapter: Generalizing Vision Transformer for Face Anti-Spoofing with Statistical Tokens","date":"2023-09-07","arxiv_id":"2309.04038","n_code_links":3,"syntology":null},{"paper":null,"slug":"short-term-load-forecasting-using-a-particle","title":"Short-Term Load Forecasting Using A Particle-Swarm Optimized Multi-Head Attention-Augmented CNN-LSTM Network","date":"2023-09-07","arxiv_id":"2309.03694","n_code_links":0,"syntology":null},{"paper":"/paper/zero-shot-audio-captioning-via-audibility","slug":"zero-shot-audio-captioning-via-audibility","title":"Zero-Shot Audio Captioning via Audibility Guidance","date":"2023-09-07","arxiv_id":"2309.03884","n_code_links":0,"syntology":null},{"paper":"/paper/certifying-llm-safety-against-adversarial","slug":"certifying-llm-safety-against-adversarial","title":"Certifying LLM Safety against Adversarial Prompting","date":"2023-09-06","arxiv_id":"2309.02705","n_code_links":1,"syntology":null},{"paper":"/paper/character-queries-a-transformer-based","slug":"character-queries-a-transformer-based","title":"Character Queries: A Transformer-based Approach to On-Line Handwritten Character Segmentation","date":"2023-09-06","arxiv_id":"2309.03072","n_code_links":1,"syntology":null},{"paper":"/paper/gpt-can-solve-mathematical-problems-without-a","slug":"gpt-can-solve-mathematical-problems-without-a","title":"GPT Can Solve Mathematical Problems Without a Calculator","date":"2023-09-06","arxiv_id":"2309.03241","n_code_links":1,"syntology":{"ran":1,"of":1,"n_ran_checked":1,"n_instrument":0,"unverified":0,"pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["thudm/mathglm"],"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":"/paper/hae-rae-bench-evaluation-of-korean-knowledge","slug":"hae-rae-bench-evaluation-of-korean-knowledge","title":"HAE-RAE Bench: Evaluation of Korean Knowledge in Language Models","date":"2023-09-06","arxiv_id":"2309.02706","n_code_links":1,"syntology":null},{"paper":null,"slug":"improving-diagnosis-and-prognosis-of-lung","title":"Improving diagnosis and prognosis of lung cancer using vision transformers: A scoping review","date":"2023-09-06","arxiv_id":"2309.02783","n_code_links":0,"syntology":null},{"paper":null,"slug":"knowledge-solver-teaching-llms-to-search-for","title":"Knowledge Solver: Teaching LLMs to Search for Domain Knowledge from Knowledge Graphs","date":"2023-09-06","arxiv_id":"2309.03118","n_code_links":0,"syntology":null},{"paper":"/paper/large-language-models-for-automated-open","slug":"large-language-models-for-automated-open","title":"Large Language Models for Automated Open-domain Scientific Hypotheses Discovery","date":"2023-09-06","arxiv_id":"2309.02726","n_code_links":1,"syntology":{"ran":6,"of":11,"n_ran_checked":6,"n_instrument":0,"unverified":5,"pointer_only":11,"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) · 5 unverified","official":{"repos":["zongliny/moose"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":5,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"leave-no-place-behind-improved-geolocation-in","title":"Leave no Place Behind: Improved Geolocation in Humanitarian Documents","date":"2023-09-06","arxiv_id":"2309.02914","n_code_links":0,"syntology":null},{"paper":"/paper/offensive-hebrew-corpus-and-detection-using","slug":"offensive-hebrew-corpus-and-detection-using","title":"Offensive Hebrew Corpus and Detection using BERT","date":"2023-09-06","arxiv_id":"2309.02724","n_code_links":1,"syntology":null},{"paper":"/paper/prompt-based-all-in-one-image-restoration","slug":"prompt-based-all-in-one-image-restoration","title":"Prompt-based Ingredient-Oriented All-in-One Image Restoration","date":"2023-09-06","arxiv_id":"2309.03063","n_code_links":1,"syntology":{"ran":11,"of":12,"n_ran_checked":11,"n_instrument":0,"unverified":1,"pointer_only":12,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 0 honoured, 0 violated, 11 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","official":{"repos":["Tombs98/CAPTNet"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":11,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"self-supervised-masked-digital-elevation","title":"Self-Supervised Masked Digital Elevation Models Encoding for Low-Resource Downstream Tasks","date":"2023-09-06","arxiv_id":"2309.03367","n_code_links":0,"syntology":null},{"paper":null,"slug":"tfbest-dual-aspect-transformer-with-learnable","title":"TFBEST: Dual-Aspect Transformer with Learnable Positional Encoding for Failure Prediction","date":"2023-09-06","arxiv_id":"2309.02641","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-survey-on-efficient-vision-transformers","title":"A survey on efficient vision transformers: algorithms, techniques, and performance benchmarking","date":"2023-09-05","arxiv_id":"2309.02031","n_code_links":0,"syntology":null},{"paper":"/paper/beetle-a-framework-for-linear-b-cell-epitope","slug":"beetle-a-framework-for-linear-b-cell-epitope","title":"BeeTLe: A Framework for Linear B-Cell Epitope Prediction and Classification","date":"2023-09-05","arxiv_id":"2309.02071","n_code_links":1,"syntology":null},{"paper":"/paper/codeapex-a-bilingual-programming-evaluation","slug":"codeapex-a-bilingual-programming-evaluation","title":"CodeApex: A Bilingual Programming Evaluation Benchmark for Large Language Models","date":"2023-09-05","arxiv_id":"2309.01940","n_code_links":1,"syntology":null},{"paper":"/paper/compressing-vision-transformers-for-low","slug":"compressing-vision-transformers-for-low","title":"Compressing Vision Transformers for Low-Resource Visual Learning","date":"2023-09-05","arxiv_id":"2309.02617","n_code_links":1,"syntology":null},{"paper":"/paper/data-juicer-a-one-stop-data-processing-system","slug":"data-juicer-a-one-stop-data-processing-system","title":"Data-Juicer: A One-Stop Data Processing System for Large Language Models","date":"2023-09-05","arxiv_id":"2309.02033","n_code_links":2,"syntology":null},{"paper":null,"slug":"dense-object-grounding-in-3d-scenes","title":"Dense Object Grounding in 3D Scenes","date":"2023-09-05","arxiv_id":"2309.02224","n_code_links":0,"syntology":null},{"paper":null,"slug":"do-you-trust-chatgpt-perceived-credibility-of","title":"Do You Trust ChatGPT? -- Perceived Credibility of Human and AI-Generated Content","date":"2023-09-05","arxiv_id":"2309.02524","n_code_links":0,"syntology":null},{"paper":null,"slug":"domain-adaptation-for-efficiently-fine-tuning","title":"Domain Adaptation for Efficiently Fine-tuning Vision Transformer with Encrypted Images","date":"2023-09-05","arxiv_id":"2309.02556","n_code_links":0,"syntology":null},{"paper":"/paper/dynamic-brain-transformer-with-multi-level","slug":"dynamic-brain-transformer-with-multi-level","title":"Dynamic Brain Transformer with Multi-level Attention for Functional Brain Network Analysis","date":"2023-09-05","arxiv_id":"2309.01941","n_code_links":1,"syntology":null},{"paper":null,"slug":"evaluation-kidney-layer-segmentation-on-whole","title":"Evaluation Kidney Layer Segmentation on Whole Slide Imaging using Convolutional Neural Networks and Transformers","date":"2023-09-05","arxiv_id":"2309.02563","n_code_links":0,"syntology":null},{"paper":"/paper/exchanging-based-multimodal-fusion-with","slug":"exchanging-based-multimodal-fusion-with","title":"Exchanging-based Multimodal Fusion with Transformer","date":"2023-09-05","arxiv_id":"2309.02190","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: 2 honoured, 1 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["recklessronan/muse"],"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/extract-and-adaptation-network-for-3d","slug":"extract-and-adaptation-network-for-3d","title":"Extract-and-Adaptation Network for 3D Interacting Hand Mesh Recovery","date":"2023-09-05","arxiv_id":"2309.01943","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":["jkpark0825/eanet"],"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":"incorporating-dictionaries-into-a-neural","title":"Incorporating Dictionaries into a Neural Network Architecture to Extract COVID-19 Medical Concepts From Social Media","date":"2023-09-05","arxiv_id":"2309.02188","n_code_links":0,"syntology":null},{"paper":null,"slug":"language-models-for-novelty-detection-in","title":"Language Models for Novelty Detection in System Call Traces","date":"2023-09-05","arxiv_id":"2309.02206","n_code_links":0,"syntology":null},{"paper":"/paper/learning-cross-modal-affinity-for-referring","slug":"learning-cross-modal-affinity-for-referring","title":"Learning Cross-Modal Affinity for Referring Video Object Segmentation Targeting Limited Samples","date":"2023-09-05","arxiv_id":"2309.02041","n_code_links":1,"syntology":null},{"paper":null,"slug":"leveraging-bert-language-models-for-multi","title":"Leveraging BERT Language Models for Multi-Lingual ESG Issue Identification","date":"2023-09-05","arxiv_id":"2309.02189","n_code_links":0,"syntology":null},{"paper":"/paper/ma-vae-multi-head-attention-based-variational","slug":"ma-vae-multi-head-attention-based-variational","title":"MA-VAE: Multi-head Attention-based Variational Autoencoder Approach for Anomaly Detection in Multivariate Time-series Applied to Automotive Endurance Powertrain Testing","date":"2023-09-05","arxiv_id":"2309.02253","n_code_links":1,"syntology":null},{"paper":"/paper/nanot5-a-pytorch-framework-for-pre-training","slug":"nanot5-a-pytorch-framework-for-pre-training","title":"nanoT5: A PyTorch Framework for Pre-training and Fine-tuning T5-style Models with Limited Resources","date":"2023-09-05","arxiv_id":"2309.02373","n_code_links":1,"syntology":{"ran":6,"of":9,"n_ran_checked":6,"n_instrument":0,"unverified":3,"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) · 3 unverified","official":{"repos":["piotrnawrot/nanot5"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":3,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"on-the-planning-search-and-memorization","title":"On the Planning, Search, and Memorization Capabilities of Large Language Models","date":"2023-09-05","arxiv_id":"2309.01868","n_code_links":0,"syntology":null},{"paper":"/paper/revisiting-file-context-for-source-code","slug":"revisiting-file-context-for-source-code","title":"Revisiting File Context for Source Code Summarization","date":"2023-09-05","arxiv_id":"2309.02326","n_code_links":1,"syntology":null},{"paper":null,"slug":"sample-size-in-natural-language-processing","title":"Sample Size in Natural Language Processing within Healthcare Research","date":"2023-09-05","arxiv_id":"2309.02237","n_code_links":0,"syntology":null},{"paper":"/paper/benchmarking-large-language-models-in","slug":"benchmarking-large-language-models-in","title":"Benchmarking Large Language Models in Retrieval-Augmented Generation","date":"2023-09-04","arxiv_id":"2309.01431","n_code_links":1,"syntology":{"ran":3,"of":3,"n_ran_checked":2,"n_instrument":1,"unverified":0,"pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 1 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["chen700564/RGB"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/dat-spatially-dynamic-vision-transformer-with","slug":"dat-spatially-dynamic-vision-transformer-with","title":"DAT++: Spatially Dynamic Vision Transformer with Deformable Attention","date":"2023-09-04","arxiv_id":"2309.01430","n_code_links":1,"syntology":{"ran":8,"of":10,"n_ran_checked":8,"n_instrument":0,"unverified":2,"pointer_only":6,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 1 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","official":{"repos":["leaplabthu/dat"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"do-androids-dream-of-fictional-references-a","title":"Do androids dream of fictional references? A bibliographic dialogue with ChatGPT3.5","date":"2023-09-04","arxiv_id":"2312.00789","n_code_links":0,"syntology":null},{"paper":null,"slug":"exmobilevit-lightweight-classifier-extension","title":"ExMobileViT: Lightweight Classifier Extension for Mobile Vision Transformer","date":"2023-09-04","arxiv_id":"2309.01310","n_code_links":0,"syntology":null},{"paper":null,"slug":"interdisciplinary-fairness-in-imbalanced","title":"Interdisciplinary Fairness in Imbalanced Research Proposal Topic Inference: A Hierarchical Transformer-based Method with Selective Interpolation","date":"2023-09-04","arxiv_id":"2309.01717","n_code_links":0,"syntology":null},{"paper":"/paper/locality-aware-hyperspectral-classification","slug":"locality-aware-hyperspectral-classification","title":"Locality-Aware Hyperspectral Classification","date":"2023-09-04","arxiv_id":"2309.01561","n_code_links":1,"syntology":null},{"paper":null,"slug":"multi-dimension-unified-swin-transformer-for","title":"Multi-dimension unified Swin Transformer for 3D Lesion Segmentation in Multiple Anatomical Locations","date":"2023-09-04","arxiv_id":"2309.01823","n_code_links":0,"syntology":null},{"paper":null,"slug":"one-wide-feedforward-is-all-you-need","title":"One Wide Feedforward is All You Need","date":"2023-09-04","arxiv_id":"2309.01826","n_code_links":0,"syntology":null},{"paper":"/paper/prompting-or-fine-tuning-a-comparative-study","slug":"prompting-or-fine-tuning-a-comparative-study","title":"Prompting or Fine-tuning? A Comparative Study of Large Language Models for Taxonomy Construction","date":"2023-09-04","arxiv_id":"2309.01715","n_code_links":1,"syntology":null},{"paper":null,"slug":"semantic-constraint-matching-transformer-for","title":"Semantic-Constraint Matching Transformer for Weakly Supervised Object Localization","date":"2023-09-04","arxiv_id":"2309.01331","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-study-on-the-implementation-of-generative","title":"A Study on the Implementation of Generative AI Services Using an Enterprise Data-Based LLM Application Architecture","date":"2023-09-03","arxiv_id":"2309.01105","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-visual-interpretation-based-self-improved","title":"A Visual Interpretation-Based Self-Improved Classification System Using Virtual Adversarial Training","date":"2023-09-03","arxiv_id":"2309.01196","n_code_links":0,"syntology":null},{"paper":null,"slug":"bias-assessment-and-mitigation-in-llm-based","title":"Bias Testing and Mitigation in LLM-based Code Generation","date":"2023-09-03","arxiv_id":"2309.14345","n_code_links":0,"syntology":null},{"paper":null,"slug":"holistic-dynamic-frequency-transformer-for","title":"Holistic Dynamic Frequency Transformer for Image Fusion and Exposure Correction","date":"2023-09-03","arxiv_id":"2309.01183","n_code_links":0,"syntology":null},{"paper":null,"slug":"magma-music-aligned-generative-motion","title":"MAGMA: Music Aligned Generative Motion Autodecoder","date":"2023-09-03","arxiv_id":"2309.01202","n_code_links":0,"syntology":null},{"paper":null,"slug":"multidomain-transformer-based-deep-learning","title":"Multidomain transformer-based deep learning for early detection of network intrusion","date":"2023-09-03","arxiv_id":"2309.01070","n_code_links":0,"syntology":null},{"paper":"/paper/saturn-an-optimized-data-system-for-large","slug":"saturn-an-optimized-data-system-for-large","title":"Saturn: An Optimized Data System for Large Model Deep Learning Workloads","date":"2023-09-03","arxiv_id":"2309.01226","n_code_links":1,"syntology":null},{"paper":"/paper/contrastive-feature-masking-open-vocabulary","slug":"contrastive-feature-masking-open-vocabulary","title":"Contrastive Feature Masking Open-Vocabulary Vision Transformer","date":"2023-09-02","arxiv_id":"2309.00775","n_code_links":0,"syntology":null},{"paper":"/paper/contrastive-grouping-with-transformer-for-1","slug":"contrastive-grouping-with-transformer-for-1","title":"Contrastive Grouping with Transformer for Referring Image Segmentation","date":"2023-09-02","arxiv_id":"2309.01017","n_code_links":1,"syntology":{"ran":22,"of":23,"n_ran_checked":13,"n_instrument":9,"unverified":1,"pointer_only":10,"phrase":"22 ran (of which 0 constructed an object rather than computing a result; 13 with no instrument failure: 0 honoured, 0 violated, 13 with no contract checked; 9 where Syntology's instrument failed) · 1 unverified","official":{"repos":["toneyaya/cgformer"],"state":"official (archive's flag): 22 ran","n_ran":22,"n_constructed":0,"n_ran_no_instrument_failure":13,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"knowledge-graph-embeddings-for-multi-lingual","title":"Knowledge Graph Embeddings for Multi-Lingual Structured Representations of Radiology Reports","date":"2023-09-02","arxiv_id":"2309.00917","n_code_links":0,"syntology":null},{"paper":null,"slug":"renaissance-a-survey-into-ai-text-to-image","title":"RenAIssance: A Survey into AI Text-to-Image Generation in the Era of Large Model","date":"2023-09-02","arxiv_id":"2309.00810","n_code_links":0,"syntology":null},{"paper":null,"slug":"studying-the-impacts-of-pre-training-using","title":"Studying the impacts of pre-training using ChatGPT-generated text on downstream tasks","date":"2023-09-02","arxiv_id":"2309.05668","n_code_links":0,"syntology":null},{"paper":"/paper/value-kaleidoscope-engaging-ai-with","slug":"value-kaleidoscope-engaging-ai-with","title":"Value Kaleidoscope: Engaging AI with Pluralistic Human Values, Rights, and Duties","date":"2023-09-02","arxiv_id":"2309.00779","n_code_links":1,"syntology":null},{"paper":"/paper/batchprompt-accomplish-more-with-less","slug":"batchprompt-accomplish-more-with-less","title":"BatchPrompt: Accomplish more with less","date":"2023-09-01","arxiv_id":"2309.00384","n_code_links":1,"syntology":null},{"paper":"/paper/dacl10k-benchmark-for-semantic-bridge-damage","slug":"dacl10k-benchmark-for-semantic-bridge-damage","title":"dacl10k: Benchmark for Semantic Bridge Damage Segmentation","date":"2023-09-01","arxiv_id":"2309.00460","n_code_links":1,"syntology":null},{"paper":null,"slug":"geometry-aware-line-graph-transformer-pre","title":"Geometry-aware Line Graph Transformer Pre-training for Molecular Property Prediction","date":"2023-09-01","arxiv_id":"2309.00483","n_code_links":0,"syntology":null},{"paper":null,"slug":"large-language-models-for-semantic-monitoring","title":"Large Language Models for Semantic Monitoring of Corporate Disclosures: A Case Study on Korea's Top 50 KOSPI Companies","date":"2023-09-01","arxiv_id":"2309.00208","n_code_links":0,"syntology":null},{"paper":"/paper/publicly-shareable-clinical-large-language","slug":"publicly-shareable-clinical-large-language","title":"Publicly Shareable Clinical Large Language Model Built on Synthetic Clinical Notes","date":"2023-09-01","arxiv_id":"2309.00237","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":["starmpcc/asclepius"],"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":"sortednet-a-place-for-every-network-and-every","title":"SortedNet: A Scalable and Generalized Framework for Training Modular Deep Neural Networks","date":"2023-09-01","arxiv_id":"2309.00255","n_code_links":0,"syntology":null},{"paper":"/paper/taken-out-of-context-on-measuring-situational","slug":"taken-out-of-context-on-measuring-situational","title":"Taken out of context: On measuring situational awareness in LLMs","date":"2023-09-01","arxiv_id":"2309.00667","n_code_links":1,"syntology":{"ran":9,"of":9,"n_ran_checked":9,"n_instrument":0,"unverified":0,"pointer_only":9,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["asacooperstickland/situational-awareness-evals"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/where-did-the-gap-go-reassessing-the-long","slug":"where-did-the-gap-go-reassessing-the-long","title":"Where Did the Gap Go? Reassessing the Long-Range Graph Benchmark","date":"2023-09-01","arxiv_id":"2309.00367","n_code_links":2,"syntology":{"ran":8,"of":12,"n_ran_checked":8,"n_instrument":0,"unverified":4,"pointer_only":6,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 2 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","official":{"repos":["toenshoff/lrgb"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":3,"ran_from_kinds":["listed","official"]}}},{"paper":null,"slug":"why-do-universal-adversarial-attacks-work-on","title":"Why do universal adversarial attacks work on large language models?: Geometry might be the answer","date":"2023-09-01","arxiv_id":"2309.00254","n_code_links":0,"syntology":null},{"paper":"/paper/beyond-self-attention-deformable-large-kernel","slug":"beyond-self-attention-deformable-large-kernel","title":"Beyond Self-Attention: Deformable Large Kernel Attention for Medical Image Segmentation","date":"2023-08-31","arxiv_id":"2309.00121","n_code_links":1,"syntology":null},{"paper":"/paper/biocoder-a-benchmark-for-bioinformatics-code","slug":"biocoder-a-benchmark-for-bioinformatics-code","title":"BioCoder: A Benchmark for Bioinformatics Code Generation with Large Language Models","date":"2023-08-31","arxiv_id":"2308.16458","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":["gersteinlab/biocoder"],"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"]}}}],"record_sha256":"39c52ddd13420a3278eeb24d841619dea44ab288faba3a7b0da416e5f69770f5","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}