{"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/142","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":142,"pages_in_order":285,"rows_per_page":100,"rows":[14101,14200],"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/141","next":"/method/residual-connection/papers/143","papers":[{"paper":"/paper/unlocking-low-light-rainy-image-restoration","slug":"unlocking-low-light-rainy-image-restoration","title":"Dual Degradation Representation for Joint Deraining and Low-Light Enhancement in the Dark","date":"2023-05-06","arxiv_id":"2305.03997","n_code_links":1,"syntology":null},{"paper":"/paper/a-transformer-based-method-for-zero-and-few","slug":"a-transformer-based-method-for-zero-and-few","title":"From Zero to Hero: Harnessing Transformers for Biomedical Named Entity Recognition in Zero- and Few-shot Contexts","date":"2023-05-05","arxiv_id":"2305.04928","n_code_links":1,"syntology":null},{"paper":null,"slug":"adapting-transformer-language-models-for","title":"Adapting Transformer Language Models for Predictive Typing in Brain-Computer Interfaces","date":"2023-05-05","arxiv_id":"2305.03819","n_code_links":0,"syntology":null},{"paper":null,"slug":"an-ensemble-of-convolution-based-methods-for","title":"An ensemble of convolution-based methods for fault detection using vibration signals","date":"2023-05-05","arxiv_id":"2305.05532","n_code_links":0,"syntology":null},{"paper":null,"slug":"block-the-label-and-noise-an-n-gram-masked","title":"Block the Label and Noise: An N-Gram Masked Speller for Chinese Spell Checking","date":"2023-05-05","arxiv_id":"2305.03314","n_code_links":0,"syntology":null},{"paper":null,"slug":"clac-at-semeval-2023-task-2-comparing-span","title":"CLaC at SemEval-2023 Task 2: Comparing Span-Prediction and Sequence-Labeling approaches for NER","date":"2023-05-05","arxiv_id":"2305.03845","n_code_links":0,"syntology":null},{"paper":null,"slug":"fm-vit-flexible-modal-vision-transformers-for","title":"FM-ViT: Flexible Modal Vision Transformers for Face Anti-Spoofing","date":"2023-05-05","arxiv_id":"2305.03277","n_code_links":0,"syntology":null},{"paper":null,"slug":"harnessing-the-power-of-bert-in-the-turkish","title":"Harnessing the Power of BERT in the Turkish Clinical Domain: Pretraining Approaches for Limited Data Scenarios","date":"2023-05-05","arxiv_id":"2305.03788","n_code_links":0,"syntology":null},{"paper":"/paper/lmeye-an-interactive-perception-network-for","slug":"lmeye-an-interactive-perception-network-for","title":"LMEye: An Interactive Perception Network for Large Language Models","date":"2023-05-05","arxiv_id":"2305.03701","n_code_links":1,"syntology":null},{"paper":null,"slug":"logo-former-local-global-spatio-temporal","title":"LOGO-Former: Local-Global Spatio-Temporal Transformer for Dynamic Facial Expression Recognition","date":"2023-05-05","arxiv_id":"2305.03343","n_code_links":0,"syntology":null},{"paper":"/paper/mindgames-targeting-theory-of-mind-in-large","slug":"mindgames-targeting-theory-of-mind-in-large","title":"MindGames: Targeting Theory of Mind in Large Language Models with Dynamic Epistemic Modal Logic","date":"2023-05-05","arxiv_id":"2305.03353","n_code_links":2,"syntology":null},{"paper":"/paper/neuromodulation-gated-transformer","slug":"neuromodulation-gated-transformer","title":"Neuromodulation Gated Transformer","date":"2023-05-05","arxiv_id":"2305.03232","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":["kobeknowles/neuromodulation-gated-transformer"],"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":"online-gesture-recognition-using-transformer","title":"Online Gesture Recognition using Transformer and Natural Language Processing","date":"2023-05-05","arxiv_id":"2305.03407","n_code_links":0,"syntology":null},{"paper":"/paper/otter-a-multi-modal-model-with-in-context","slug":"otter-a-multi-modal-model-with-in-context","title":"Otter: A Multi-Modal Model with In-Context Instruction Tuning","date":"2023-05-05","arxiv_id":"2305.03726","n_code_links":1,"syntology":null},{"paper":null,"slug":"predicting-covid-19-and-pneumonia","title":"Predicting COVID-19 and pneumonia complications from admission texts","date":"2023-05-05","arxiv_id":"2305.03661","n_code_links":0,"syntology":null},{"paper":"/paper/reduction-of-class-activation-uncertainty","slug":"reduction-of-class-activation-uncertainty","title":"Reduction of Class Activation Uncertainty with Background Information","date":"2023-05-05","arxiv_id":"2305.03238","n_code_links":2,"syntology":null},{"paper":null,"slug":"retrieval-augmented-chest-x-ray-report","title":"Retrieval Augmented Chest X-Ray Report Generation using OpenAI GPT models","date":"2023-05-05","arxiv_id":"2305.03660","n_code_links":0,"syntology":null},{"paper":null,"slug":"segmentation-of-fundus-vascular-images-based","title":"MAF-Net: Multiple attention-guided fusion network for fundus vascular image segmentation","date":"2023-05-05","arxiv_id":"2305.03617","n_code_links":0,"syntology":null},{"paper":null,"slug":"simulating-h-p-lovecraft-horror-literature","title":"Simulating H.P. Lovecraft horror literature with the ChatGPT large language model","date":"2023-05-05","arxiv_id":"2305.03429","n_code_links":0,"syntology":null},{"paper":"/paper/the-muse-2023-multimodal-sentiment-analysis","slug":"the-muse-2023-multimodal-sentiment-analysis","title":"The MuSe 2023 Multimodal Sentiment Analysis Challenge: Mimicked Emotions, Cross-Cultural Humour, and Personalisation","date":"2023-05-05","arxiv_id":"2305.03369","n_code_links":1,"syntology":null},{"paper":null,"slug":"transformer-working-memory-enables-regular","title":"Transformer Working Memory Enables Regular Language Reasoning and Natural Language Length Extrapolation","date":"2023-05-05","arxiv_id":"2305.03796","n_code_links":0,"syntology":null},{"paper":"/paper/using-chatgpt-for-entity-matching","slug":"using-chatgpt-for-entity-matching","title":"Using ChatGPT for Entity Matching","date":"2023-05-05","arxiv_id":"2305.03423","n_code_links":1,"syntology":null},{"paper":"/paper/verify-and-edit-a-knowledge-enhanced-chain-of","slug":"verify-and-edit-a-knowledge-enhanced-chain-of","title":"Verify-and-Edit: A Knowledge-Enhanced Chain-of-Thought Framework","date":"2023-05-05","arxiv_id":"2305.03268","n_code_links":1,"syntology":null},{"paper":"/paper/2x-faster-language-model-pre-training-via","slug":"2x-faster-language-model-pre-training-via","title":"Masked Structural Growth for 2x Faster Language Model Pre-training","date":"2023-05-04","arxiv_id":"2305.02869","n_code_links":1,"syntology":{"ran":16,"of":34,"n_ran_checked":10,"n_instrument":6,"unverified":18,"pointer_only":0,"phrase":"16 ran (of which 6 constructed an object rather than computing a result; 10 with no instrument failure: 0 honoured, 0 violated, 10 with no contract checked; 6 where Syntology's instrument failed) · 18 unverified","official":{"repos":["cofe-ai/msg"],"state":"official (archive's flag): 16 ran","n_ran":16,"n_constructed":6,"n_ran_no_instrument_failure":10,"n_unverified":18,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"an-automatically-discovered-chain-of-thought","title":"An automatically discovered chain-of-thought prompt generalizes to novel models and datasets","date":"2023-05-04","arxiv_id":"2305.02897","n_code_links":0,"syntology":null},{"paper":null,"slug":"automl-gpt-automatic-machine-learning-with","title":"AutoML-GPT: Automatic Machine Learning with GPT","date":"2023-05-04","arxiv_id":"2305.02499","n_code_links":0,"syntology":null},{"paper":null,"slug":"branchnorm-robustly-scaling-extremely-deep","title":"BranchNorm: Robustly Scaling Extremely Deep Transformers","date":"2023-05-04","arxiv_id":"2305.02790","n_code_links":0,"syntology":null},{"paper":null,"slug":"breast-cancer-diagnosis-using-machine","title":"Breast Cancer Diagnosis Using Machine Learning Techniques","date":"2023-05-04","arxiv_id":"2305.02482","n_code_links":0,"syntology":null},{"paper":"/paper/catch-missing-details-image-reconstruction","slug":"catch-missing-details-image-reconstruction","title":"Catch Missing Details: Image Reconstruction with Frequency Augmented Variational Autoencoder","date":"2023-05-04","arxiv_id":"2305.02541","n_code_links":1,"syntology":{"ran":13,"of":15,"n_ran_checked":11,"n_instrument":2,"unverified":2,"pointer_only":3,"phrase":"13 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 0 honoured, 3 violated, 8 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","official":{"repos":["oppo-us-research/FA-VAE"],"state":"official (archive's flag): 13 ran","n_ran":13,"n_constructed":0,"n_ran_no_instrument_failure":11,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":"/paper/chain-of-skills-a-configurable-model-for-open","slug":"chain-of-skills-a-configurable-model-for-open","title":"Chain-of-Skills: A Configurable Model for Open-domain Question Answering","date":"2023-05-04","arxiv_id":"2305.03130","n_code_links":1,"syntology":null},{"paper":null,"slug":"enhancing-pashto-text-classification-using","title":"Enhancing Pashto Text Classification using Language Processing Techniques for Single And Multi-Label Analysis","date":"2023-05-04","arxiv_id":"2305.03201","n_code_links":0,"syntology":null},{"paper":null,"slug":"gpt-4-a-review-on-advancements-and","title":"Gpt-4: A Review on Advancements and Opportunities in Natural Language Processing","date":"2023-05-04","arxiv_id":"2305.03195","n_code_links":0,"syntology":null},{"paper":null,"slug":"hierarchical-transformer-for-scalable-graph","title":"Hierarchical Transformer for Scalable Graph Learning","date":"2023-05-04","arxiv_id":"2305.02866","n_code_links":0,"syntology":null},{"paper":"/paper/improving-code-example-recommendations-on","slug":"improving-code-example-recommendations-on","title":"Improving Code Example Recommendations on Informal Documentation Using BERT and Query-Aware LSH: A Comparative Study","date":"2023-05-04","arxiv_id":"2305.03017","n_code_links":1,"syntology":null},{"paper":null,"slug":"interpretable-sentence-representation-with","title":"Interpretable Sentence Representation with Variational Autoencoders and Attention","date":"2023-05-04","arxiv_id":"2305.02810","n_code_links":0,"syntology":null},{"paper":null,"slug":"language-time-preferences-and-consumer","title":"Can LLMs Capture Human Preferences?","date":"2023-05-04","arxiv_id":"2305.02531","n_code_links":0,"syntology":null},{"paper":null,"slug":"late-binding-scholarship-in-the-age-of-ai","title":"Late-Binding Scholarship in the Age of AI: Navigating Legal and Normative Challenges of a New Form of Knowledge Production","date":"2023-05-04","arxiv_id":"2305.11058","n_code_links":0,"syntology":null},{"paper":null,"slug":"learning-language-specific-layers-for","title":"Learning Language-Specific Layers for Multilingual Machine Translation","date":"2023-05-04","arxiv_id":"2305.02665","n_code_links":0,"syntology":null},{"paper":null,"slug":"leveraging-bert-language-model-for-arabic","title":"Leveraging BERT Language Model for Arabic Long Document Classification","date":"2023-05-04","arxiv_id":"2305.03519","n_code_links":0,"syntology":null},{"paper":null,"slug":"noise-resistant-multimodal-transformer-for","title":"Noise-Resistant Multimodal Transformer for Emotion Recognition","date":"2023-05-04","arxiv_id":"2305.02814","n_code_links":0,"syntology":null},{"paper":"/paper/personallm-investigating-the-ability-of-gpt-3","slug":"personallm-investigating-the-ability-of-gpt-3","title":"PersonaLLM: Investigating the Ability of Large Language Models to Express Personality Traits","date":"2023-05-04","arxiv_id":"2305.02547","n_code_links":1,"syntology":{"ran":2,"of":2,"n_ran_checked":0,"n_instrument":2,"unverified":0,"pointer_only":0,"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":["hjian42/personallm"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"point-transformer-for-coronary-artery","title":"Point Transformer For Coronary Artery Labeling","date":"2023-05-04","arxiv_id":"2305.02533","n_code_links":0,"syntology":null},{"paper":null,"slug":"self-supervised-learning-for-organs-at-risk","title":"Self-Supervised Learning for Organs At Risk and Tumor Segmentation with Uncertainty Quantification","date":"2023-05-04","arxiv_id":"2305.02491","n_code_links":0,"syntology":null},{"paper":null,"slug":"the-application-of-affective-measures-in-text","title":"The Application of Affective Measures in Text-based Emotion Aware Recommender Systems","date":"2023-05-04","arxiv_id":"2305.04796","n_code_links":0,"syntology":null},{"paper":null,"slug":"updexplainer-an-interpretable-transformer","title":"UPDExplainer: an Interpretable Transformer-based Framework for Urban Physical Disorder Detection Using Street View Imagery","date":"2023-05-04","arxiv_id":"2305.02911","n_code_links":0,"syntology":null},{"paper":"/paper/a-lightweight-cnn-transformer-model-for","slug":"a-lightweight-cnn-transformer-model-for","title":"A Lightweight CNN-Transformer Model for Learning Traveling Salesman Problems","date":"2023-05-03","arxiv_id":"2305.01883","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-novel-plagiarism-detection-approach","title":"A Novel Plagiarism Detection Approach Combining BERT-based Word Embedding, Attention-based LSTMs and an Improved Differential Evolution Algorithm","date":"2023-05-03","arxiv_id":"2305.02374","n_code_links":0,"syntology":null},{"paper":"/paper/a-systematic-study-of-knowledge-distillation","slug":"a-systematic-study-of-knowledge-distillation","title":"A Systematic Study of Knowledge Distillation for Natural Language Generation with Pseudo-Target Training","date":"2023-05-03","arxiv_id":"2305.02031","n_code_links":1,"syntology":{"ran":2,"of":4,"n_ran_checked":2,"n_instrument":0,"unverified":2,"pointer_only":0,"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) · 2 unverified","official":{"repos":["nitaytech/kd4gen"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":"/paper/a-vision-transformer-approach-for-efficient","slug":"a-vision-transformer-approach-for-efficient","title":"A Vision Transformer Approach for Efficient Near-Field Irregular SAR Super-Resolution","date":"2023-05-03","arxiv_id":"2305.02074","n_code_links":2,"syntology":null},{"paper":"/paper/alleviating-exposure-bias-via-multi-level","slug":"alleviating-exposure-bias-via-multi-level","title":"Alleviating Exposure Bias via Multi-level Contrastive Learning and Deviation Simulation in Abstractive Summarization","date":"2023-05-03","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"cheaply-evaluating-inference-efficiency","title":"Cheaply Evaluating Inference Efficiency Metrics for Autoregressive Transformer APIs","date":"2023-05-03","arxiv_id":"2305.02440","n_code_links":0,"syntology":null},{"paper":null,"slug":"clinical-note-generation-from-doctor-patient","title":"WangLab at MEDIQA-Chat 2023: Clinical Note Generation from Doctor-Patient Conversations using Large Language Models","date":"2023-05-03","arxiv_id":"2305.02220","n_code_links":0,"syntology":null},{"paper":"/paper/distilling-step-by-step-outperforming-larger","slug":"distilling-step-by-step-outperforming-larger","title":"Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes","date":"2023-05-03","arxiv_id":"2305.02301","n_code_links":1,"syntology":{"ran":4,"of":4,"n_ran_checked":4,"n_instrument":0,"unverified":0,"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) · 0 unverified","official":{"repos":["google-research/distilling-step-by-step"],"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":["official"]}}},{"paper":"/paper/entity-tracking-in-language-models","slug":"entity-tracking-in-language-models","title":"Entity Tracking in Language Models","date":"2023-05-03","arxiv_id":"2305.02363","n_code_links":1,"syntology":null},{"paper":null,"slug":"evaluating-bert-and-parsbert-for-analyzing","title":"evaluating bert and parsbert for analyzing persian advertisement data","date":"2023-05-03","arxiv_id":"2305.02426","n_code_links":0,"syntology":null},{"paper":null,"slug":"evaluating-bert-based-scientific-relation","title":"Evaluating BERT-based Scientific Relation Classifiers for Scholarly Knowledge Graph Construction on Digital Library Collections","date":"2023-05-03","arxiv_id":"2305.02291","n_code_links":0,"syntology":null},{"paper":null,"slug":"exploring-linguistic-properties-of","title":"Exploring Linguistic Properties of Monolingual BERTs with Typological Classification among Languages","date":"2023-05-03","arxiv_id":"2305.02215","n_code_links":0,"syntology":null},{"paper":"/paper/glitch-in-the-matrix-a-large-scale-benchmark","slug":"glitch-in-the-matrix-a-large-scale-benchmark","title":"Glitch in the Matrix: A Large Scale Benchmark for Content Driven Audio-Visual Forgery Detection and Localization","date":"2023-05-03","arxiv_id":"2305.01979","n_code_links":1,"syntology":null},{"paper":"/paper/gpt-re-in-context-learning-for-relation","slug":"gpt-re-in-context-learning-for-relation","title":"GPT-RE: In-context Learning for Relation Extraction using Large Language Models","date":"2023-05-03","arxiv_id":"2305.02105","n_code_links":1,"syntology":null},{"paper":null,"slug":"learngene-inheriting-condensed-knowledge-from","title":"Learngene: Inheriting Condensed Knowledge from the Ancestry Model to Descendant Models","date":"2023-05-03","arxiv_id":"2305.02279","n_code_links":0,"syntology":null},{"paper":"/paper/new-adversarial-image-detection-based-on","slug":"new-adversarial-image-detection-based-on","title":"New Adversarial Image Detection Based on Sentiment Analysis","date":"2023-05-03","arxiv_id":"2305.03173","n_code_links":1,"syntology":null},{"paper":null,"slug":"towards-imperceptible-document-manipulations","title":"Towards Imperceptible Document Manipulations against Neural Ranking Models","date":"2023-05-03","arxiv_id":"2305.01860","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-study-on-the-integration-of-pipeline-and","title":"A Study on the Integration of Pipeline and E2E SLU systems for Spoken Semantic Parsing toward STOP Quality Challenge","date":"2023-05-02","arxiv_id":"2305.01620","n_code_links":0,"syntology":null},{"paper":"/paper/arbex-attentive-feature-extraction-with","slug":"arbex-attentive-feature-extraction-with","title":"ARBEx: Attentive Feature Extraction with Reliability Balancing for Robust Facial Expression Learning","date":"2023-05-02","arxiv_id":"2305.01486","n_code_links":1,"syntology":null},{"paper":null,"slug":"axwin-transformer-a-context-aware-vision","title":"AxWin Transformer: A Context-Aware Vision Transformer Backbone with Axial Windows","date":"2023-05-02","arxiv_id":"2305.01280","n_code_links":0,"syntology":null},{"paper":null,"slug":"brainnpt-pre-training-of-transformer-networks","title":"BrainNPT: Pre-training of Transformer networks for brain network classification","date":"2023-05-02","arxiv_id":"2305.01666","n_code_links":0,"syntology":null},{"paper":null,"slug":"cancer-hallmark-classification-using","title":"Improving Cancer Hallmark Classification with BERT-based Deep Learning Approach","date":"2023-05-02","arxiv_id":"2305.03501","n_code_links":0,"syntology":null},{"paper":"/paper/discern-and-answer-mitigating-the-impact-of","slug":"discern-and-answer-mitigating-the-impact-of","title":"Why So Gullible? Enhancing the Robustness of Retrieval-Augmented Models against Counterfactual Noise","date":"2023-05-02","arxiv_id":"2305.01579","n_code_links":1,"syntology":null},{"paper":null,"slug":"exploring-vision-transformer-layer-choosing","title":"Exploring vision transformer layer choosing for semantic segmentation","date":"2023-05-02","arxiv_id":"2305.01279","n_code_links":0,"syntology":null},{"paper":null,"slug":"freelm-fine-tuning-free-language-model","title":"FreeLM: Fine-Tuning-Free Language Model","date":"2023-05-02","arxiv_id":"2305.01616","n_code_links":0,"syntology":null},{"paper":null,"slug":"high-resolution-synthetic-rgb-d-datasets-for","title":"High-Resolution Synthetic RGB-D Datasets for Monocular Depth Estimation","date":"2023-05-02","arxiv_id":"2305.01732","n_code_links":0,"syntology":null},{"paper":"/paper/how-to-unleash-the-power-of-large-language","slug":"how-to-unleash-the-power-of-large-language","title":"How to Unleash the Power of Large Language Models for Few-shot Relation Extraction?","date":"2023-05-02","arxiv_id":"2305.01555","n_code_links":2,"syntology":null},{"paper":"/paper/is-your-code-generated-by-chatgpt-really-1","slug":"is-your-code-generated-by-chatgpt-really-1","title":"Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generation","date":"2023-05-02","arxiv_id":"2305.01210","n_code_links":1,"syntology":{"ran":1,"of":1,"n_ran_checked":1,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["evalplus/evalplus"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"new-trends-in-machine-translation-using-large","title":"A Paradigm Shift: The Future of Machine Translation Lies with Large Language Models","date":"2023-05-02","arxiv_id":"2305.01181","n_code_links":0,"syntology":null},{"paper":null,"slug":"the-pipeline-system-of-asr-and-nlu-with-mlm","title":"The Pipeline System of ASR and NLU with MLM-based Data Augmentation toward STOP Low-resource Challenge","date":"2023-05-02","arxiv_id":"2305.01194","n_code_links":0,"syntology":null},{"paper":"/paper/unlimiformer-long-range-transformers-with","slug":"unlimiformer-long-range-transformers-with","title":"Unlimiformer: Long-Range Transformers with Unlimited Length Input","date":"2023-05-02","arxiv_id":"2305.01625","n_code_links":2,"syntology":{"ran":4,"of":5,"n_ran_checked":1,"n_instrument":3,"unverified":1,"pointer_only":0,"phrase":"4 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; 3 where Syntology's instrument failed) · 1 unverified","official":{"repos":["abertsch72/unlimiformer"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":"/paper/vision-meets-definitions-unsupervised-visual","slug":"vision-meets-definitions-unsupervised-visual","title":"Vision Meets Definitions: Unsupervised Visual Word Sense Disambiguation Incorporating Gloss Information","date":"2023-05-02","arxiv_id":"2305.01788","n_code_links":1,"syntology":null},{"paper":null,"slug":"automated-paper-screening-for-clinical","title":"Automated Paper Screening for Clinical Reviews Using Large Language Models","date":"2023-05-01","arxiv_id":"2305.00844","n_code_links":0,"syntology":null},{"paper":"/paper/end-to-end-lane-detection-with-one-to-several","slug":"end-to-end-lane-detection-with-one-to-several","title":"End-to-End Lane detection with One-to-Several Transformer","date":"2023-05-01","arxiv_id":"2305.00675","n_code_links":3,"syntology":null},{"paper":null,"slug":"large-linguistic-models-analyzing-theoretical","title":"Large Linguistic Models: Investigating LLMs' metalinguistic abilities","date":"2023-05-01","arxiv_id":"2305.00948","n_code_links":0,"syntology":null},{"paper":null,"slug":"lcaunet-a-skin-lesion-segmentation-network","title":"LCAUnet: A skin lesion segmentation network with enhanced edge and body fusion","date":"2023-05-01","arxiv_id":"2305.00837","n_code_links":0,"syntology":null},{"paper":null,"slug":"logion-machine-learning-for-greek-philology","title":"Logion: Machine Learning for Greek Philology","date":"2023-05-01","arxiv_id":"2305.01099","n_code_links":0,"syntology":null},{"paper":"/paper/multi-scale-transformer-based-network-for","slug":"multi-scale-transformer-based-network-for","title":"Multi-scale Transformer-based Network for Emotion Recognition from Multi Physiological Signals","date":"2023-05-01","arxiv_id":"2305.00769","n_code_links":1,"syntology":null},{"paper":"/paper/neural-machine-translation-models-with","slug":"neural-machine-translation-models-with","title":"Neural Machine Translation Models with Attention-Based Dropout Layer","date":"2023-05-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/online-portfolio-management-via-deep","slug":"online-portfolio-management-via-deep","title":"Online Portfolio Management via Deep Reinforcement Learning with High-Frequency Data","date":"2023-05-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"point-cloud-semantic-segmentation","title":"Point Cloud Semantic Segmentation","date":"2023-05-01","arxiv_id":"2305.00773","n_code_links":0,"syntology":null},{"paper":"/paper/rethinking-boundary-detection-in-deep","slug":"rethinking-boundary-detection-in-deep","title":"Rethinking Boundary Detection in Deep Learning Models for Medical Image Segmentation","date":"2023-05-01","arxiv_id":"2305.00678","n_code_links":1,"syntology":null},{"paper":null,"slug":"retrieving-comparative-arguments-using","title":"Retrieving Comparative Arguments using Ensemble Methods and Neural Information Retrieval","date":"2023-05-01","arxiv_id":"2305.01513","n_code_links":0,"syntology":null},{"paper":"/paper/safewebuh-at-semeval-2023-task-11-learning","slug":"safewebuh-at-semeval-2023-task-11-learning","title":"SafeWebUH at SemEval-2023 Task 11: Learning Annotator Disagreement in Derogatory Text: Comparison of Direct Training vs Aggregation","date":"2023-05-01","arxiv_id":"2305.01050","n_code_links":1,"syntology":null},{"paper":"/paper/beyond-classification-financial-reasoning-in","slug":"beyond-classification-financial-reasoning-in","title":"Beyond Classification: Financial Reasoning in State-of-the-Art Language Models","date":"2023-04-30","arxiv_id":"2305.01505","n_code_links":1,"syntology":null},{"paper":null,"slug":"cross-shaped-windows-transformer-with-self","title":"Cross-Shaped Windows Transformer with Self-supervised Pretraining for Clinically Significant Prostate Cancer Detection in Bi-parametric MRI","date":"2023-04-30","arxiv_id":"2305.00385","n_code_links":0,"syntology":null},{"paper":"/paper/discriminative-co-saliency-and-background","slug":"discriminative-co-saliency-and-background","title":"Discriminative Co-Saliency and Background Mining Transformer for Co-Salient Object Detection","date":"2023-04-30","arxiv_id":"2305.00514","n_code_links":1,"syntology":null},{"paper":"/paper/exploring-the-effectiveness-of-large-language","slug":"exploring-the-effectiveness-of-large-language","title":"Using Large Language Models to Generate JUnit Tests: An Empirical Study","date":"2023-04-30","arxiv_id":"2305.00418","n_code_links":1,"syntology":null},{"paper":"/paper/how-does-gpt-2-compute-greater-than-1","slug":"how-does-gpt-2-compute-greater-than-1","title":"How does GPT-2 compute greater-than?: Interpreting mathematical abilities in a pre-trained language model","date":"2023-04-30","arxiv_id":"2305.00586","n_code_links":3,"syntology":null},{"paper":null,"slug":"multimodal-graph-transformer-for-multimodal","title":"Multimodal Graph Transformer for Multimodal Question Answering","date":"2023-04-30","arxiv_id":"2305.00581","n_code_links":0,"syntology":null},{"paper":"/paper/mtc-a-multi-task-model-for-encrypted-network","slug":"mtc-a-multi-task-model-for-encrypted-network","title":"MTC: A Multi-Task Model for Encrypted Network Traffic Classification Based on Transformer and 1D-CNN","date":"2023-04-29","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"3d-brainformer-3d-fusion-transformer-for","title":"3D Brainformer: 3D Fusion Transformer for Brain Tumor Segmentation","date":"2023-04-28","arxiv_id":"2304.14508","n_code_links":0,"syntology":null},{"paper":"/paper/a-positive-feedback-method-based-on-f-measure","slug":"a-positive-feedback-method-based-on-f-measure","title":"A positive feedback method based on F-measure value for Salient Object Detection","date":"2023-04-28","arxiv_id":"2304.14619","n_code_links":1,"syntology":null},{"paper":null,"slug":"an-automated-end-to-end-deep-learning-based","title":"An automated end-to-end deep learning-based framework for lung cancer diagnosis by detecting and classifying the lung nodules","date":"2023-04-28","arxiv_id":"2305.00046","n_code_links":0,"syntology":null},{"paper":"/paper/are-the-best-multilingual-document-embeddings","slug":"are-the-best-multilingual-document-embeddings","title":"Are the Best Multilingual Document Embeddings simply Based on Sentence Embeddings?","date":"2023-04-28","arxiv_id":"2304.14796","n_code_links":1,"syntology":null}],"record_sha256":"b0420c8b190fc4a5300ac162cadc3fa6240407fbf2bbf26a932bdf2bc7257b1d","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}