{"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/transformer/papers/75","list_of":"/method/transformer","method":"Transformer","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":75,"pages_in_order":140,"rows_per_page":100,"rows":[7401,7500],"of":13999,"counts":{"archive_papers_tagged":13999,"with_a_code_link":6572,"where_syntology_ran_a_sample":2248,"not_listed_spam_title":0,"listed":13999,"listed_where_code_ran":2248,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":1919,"every_run_a_failure_of_syntologys_instrument":329,"listed_with_a_run_with_no_instrument_failure":1919,"listed_every_run_a_failure_of_syntologys_instrument":329,"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/transformer","prev":"/method/transformer/papers/74","next":"/method/transformer/papers/76","papers":[{"paper":"/paper/cmb-a-comprehensive-medical-benchmark-in","slug":"cmb-a-comprehensive-medical-benchmark-in","title":"CMB: A Comprehensive Medical Benchmark in Chinese","date":"2023-08-17","arxiv_id":"2308.08833","n_code_links":2,"syntology":{"ran":0,"of":1,"n_ran_checked":0,"n_instrument":0,"unverified":1,"pointer_only":0,"phrase":"0 ran · 1 unverified","official":{"repos":["FreedomIntelligence/CMB"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"paper":null,"slug":"icar-image-based-complementary-auto-reasoning","title":"ICAR: Image-based Complementary Auto Reasoning","date":"2023-08-17","arxiv_id":"2308.09119","n_code_links":0,"syntology":null},{"paper":null,"slug":"jpeg-quantized-coefficient-recovery-via-dct","title":"JPEG Quantized Coefficient Recovery via DCT Domain Spatial-Frequential Transformer","date":"2023-08-17","arxiv_id":"2308.09110","n_code_links":0,"syntology":null},{"paper":"/paper/learning-a-coarse-to-fine-diffusion","slug":"learning-a-coarse-to-fine-diffusion","title":"Learning A Coarse-to-Fine Diffusion Transformer for Image Restoration","date":"2023-08-17","arxiv_id":"2308.08730","n_code_links":1,"syntology":null},{"paper":null,"slug":"mascqa-a-question-answering-dataset-for","title":"MaScQA: A Question Answering Dataset for Investigating Materials Science Knowledge of Large Language Models","date":"2023-08-17","arxiv_id":"2308.09115","n_code_links":0,"syntology":null},{"paper":"/paper/mindmap-knowledge-graph-prompting-sparks","slug":"mindmap-knowledge-graph-prompting-sparks","title":"MindMap: Knowledge Graph Prompting Sparks Graph of Thoughts in Large Language Models","date":"2023-08-17","arxiv_id":"2308.09729","n_code_links":1,"syntology":{"ran":6,"of":8,"n_ran_checked":6,"n_instrument":0,"unverified":2,"pointer_only":8,"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) · 2 unverified","official":{"repos":["wyl-willing/MindMap"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":"/paper/pmet-precise-model-editing-in-a-transformer","slug":"pmet-precise-model-editing-in-a-transformer","title":"PMET: Precise Model Editing in a Transformer","date":"2023-08-17","arxiv_id":"2308.08742","n_code_links":1,"syntology":{"ran":0,"of":1,"n_ran_checked":0,"n_instrument":0,"unverified":1,"pointer_only":1,"phrase":"0 ran · 1 unverified","official":{"repos":["xpq-tech/pmet"],"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/point-aware-interaction-and-cnn-induced","slug":"point-aware-interaction-and-cnn-induced","title":"Point-aware Interaction and CNN-induced Refinement Network for RGB-D Salient Object Detection","date":"2023-08-17","arxiv_id":"2308.08930","n_code_links":2,"syntology":null},{"paper":null,"slug":"semantic-information-for-object-detection","title":"Semantic Information for Object Detection","date":"2023-08-17","arxiv_id":"2308.08990","n_code_links":0,"syntology":null},{"paper":null,"slug":"sensor-fusion-by-spatial-encoding-for","title":"Sensor Fusion by Spatial Encoding for Autonomous Driving","date":"2023-08-17","arxiv_id":"2308.10707","n_code_links":0,"syntology":null},{"paper":null,"slug":"simfir-a-simple-framework-for-fisheye-image","title":"SimFIR: A Simple Framework for Fisheye Image Rectification with Self-supervised Representation Learning","date":"2023-08-17","arxiv_id":"2308.09040","n_code_links":0,"syntology":null},{"paper":null,"slug":"agglomerative-transformer-for-human-object","title":"Agglomerative Transformer for Human-Object Interaction Detection","date":"2023-08-16","arxiv_id":"2308.08370","n_code_links":0,"syntology":null},{"paper":null,"slug":"boosting-logical-reasoning-in-large-language","title":"Boosting Logical Reasoning in Large Language Models through a New Framework: The Graph of Thought","date":"2023-08-16","arxiv_id":"2308.08614","n_code_links":0,"syntology":null},{"paper":"/paper/care-a-large-scale-ct-image-dataset-and","slug":"care-a-large-scale-ct-image-dataset-and","title":"CARE: A Large Scale CT Image Dataset and Clinical Applicable Benchmark Model for Rectal Cancer Segmentation","date":"2023-08-16","arxiv_id":"2308.08283","n_code_links":0,"syntology":null},{"paper":"/paper/dsat-net-dual-spatial-attention-transformer","slug":"dsat-net-dual-spatial-attention-transformer","title":"DSAT-Net: Dual Spatial Attention Transformer for Building Extraction from Aerial Images","date":"2023-08-16","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"fast-training-of-nmt-model-with-data-sorting","title":"Fast Training of NMT Model with Data Sorting","date":"2023-08-16","arxiv_id":"2308.08153","n_code_links":0,"syntology":null},{"paper":null,"slug":"how-to-mask-in-error-correction-code","title":"How to Mask in Error Correction Code Transformer: Systematic and Double Masking","date":"2023-08-16","arxiv_id":"2308.08128","n_code_links":0,"syntology":null},{"paper":null,"slug":"improving-depth-gradient-continuity-in","title":"Improving Depth Gradient Continuity in Transformers: A Comparative Study on Monocular Depth Estimation with CNN","date":"2023-08-16","arxiv_id":"2308.08333","n_code_links":0,"syntology":null},{"paper":null,"slug":"low-light-image-enhancement-with-illumination","title":"Low-Light Image Enhancement with Illumination-Aware Gamma Correction and Complete Image Modelling Network","date":"2023-08-16","arxiv_id":"2308.08220","n_code_links":0,"syntology":null},{"paper":null,"slug":"mitigating-the-exposure-bias-in-sentence","title":"Mitigating the Exposure Bias in Sentence-Level Grapheme-to-Phoneme (G2P) Transduction","date":"2023-08-16","arxiv_id":"2308.08442","n_code_links":0,"syntology":null},{"paper":null,"slug":"radio2text-streaming-speech-recognition-using","title":"Radio2Text: Streaming Speech Recognition Using mmWave Radio Signals","date":"2023-08-16","arxiv_id":"2308.08125","n_code_links":0,"syntology":null},{"paper":null,"slug":"self-deception-reverse-penetrating-the","title":"Self-Deception: Reverse Penetrating the Semantic Firewall of Large Language Models","date":"2023-08-16","arxiv_id":"2308.11521","n_code_links":0,"syntology":null},{"paper":"/paper/tem-adapter-adapting-image-text-pretraining","slug":"tem-adapter-adapting-image-text-pretraining","title":"Tem-adapter: Adapting Image-Text Pretraining for Video Question Answer","date":"2023-08-16","arxiv_id":"2308.08414","n_code_links":1,"syntology":null},{"paper":"/paper/time-travel-in-llms-tracing-data","slug":"time-travel-in-llms-tracing-data","title":"Time Travel in LLMs: Tracing Data Contamination in Large Language Models","date":"2023-08-16","arxiv_id":"2308.08493","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 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; the one sample that ran constructed an object rather than computing a result","official":{"repos":["shahriargolchin/time-travel-in-llms"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"a-trustable-lstm-autoencoder-network-for","title":"A Trustable LSTM-Autoencoder Network for Cyberbullying Detection on Social Media Using Synthetic Data","date":"2023-08-15","arxiv_id":"2308.09722","n_code_links":0,"syntology":null},{"paper":"/paper/attention-is-not-all-you-need-anymore","slug":"attention-is-not-all-you-need-anymore","title":"Attention Is Not All You Need Anymore","date":"2023-08-15","arxiv_id":"2308.07661","n_code_links":2,"syntology":null},{"paper":null,"slug":"automated-test-case-generation-using-code","title":"Domain Adaptation for Code Model-based Unit Test Case Generation","date":"2023-08-15","arxiv_id":"2308.08033","n_code_links":0,"syntology":null},{"paper":"/paper/fast-machine-unlearning-without-retraining","slug":"fast-machine-unlearning-without-retraining","title":"Fast Machine Unlearning Without Retraining Through Selective Synaptic Dampening","date":"2023-08-15","arxiv_id":"2308.07707","n_code_links":1,"syntology":{"ran":6,"of":6,"n_ran_checked":6,"n_instrument":0,"unverified":0,"pointer_only":5,"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) · 0 unverified","official":{"repos":["if-loops/selective-synaptic-dampening"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"graph-segmenter-graph-transformer-with","title":"Graph-Segmenter: Graph Transformer with Boundary-aware Attention for Semantic Segmentation","date":"2023-08-15","arxiv_id":"2308.07592","n_code_links":0,"syntology":null},{"paper":null,"slug":"large-language-models-in-introductory","title":"Large Language Models in Introductory Programming Education: ChatGPT's Performance and Implications for Assessments","date":"2023-08-15","arxiv_id":"2308.08572","n_code_links":0,"syntology":null},{"paper":null,"slug":"learning-image-deraining-transformer-network","title":"Learning Image Deraining Transformer Network with Dynamic Dual Self-Attention","date":"2023-08-15","arxiv_id":"2308.07781","n_code_links":0,"syntology":null},{"paper":"/paper/memory-and-anticipation-transformer-for","slug":"memory-and-anticipation-transformer-for","title":"Memory-and-Anticipation Transformer for Online Action Understanding","date":"2023-08-15","arxiv_id":"2308.07893","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":["echo0125/memory-and-anticipation-transformer"],"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/seda-self-ensembling-vit-with-defensive","slug":"seda-self-ensembling-vit-with-defensive","title":"SEDA: Self-Ensembling ViT with Defensive Distillation and Adversarial Training for robust Chest X-rays Classification","date":"2023-08-15","arxiv_id":"2308.07874","n_code_links":1,"syntology":null},{"paper":"/paper/solving-challenging-math-word-problems-using","slug":"solving-challenging-math-word-problems-using","title":"Solving Challenging Math Word Problems Using GPT-4 Code Interpreter with Code-based Self-Verification","date":"2023-08-15","arxiv_id":"2308.07921","n_code_links":1,"syntology":null},{"paper":null,"slug":"sst-a-simplified-swin-transformer-based-model","title":"SST: A Simplified Swin Transformer-based Model for Taxi Destination Prediction based on Existing Trajectory","date":"2023-08-15","arxiv_id":"2308.07555","n_code_links":0,"syntology":null},{"paper":"/paper/story-visualization-by-online-text","slug":"story-visualization-by-online-text","title":"Story Visualization by Online Text Augmentation with Context Memory","date":"2023-08-15","arxiv_id":"2308.07575","n_code_links":1,"syntology":null},{"paper":null,"slug":"vbd-mt-chinese-vietnamese-translation-systems","title":"VBD-MT Chinese-Vietnamese Translation Systems for VLSP 2022","date":"2023-08-15","arxiv_id":"2308.07601","n_code_links":0,"syntology":null},{"paper":null,"slug":"approximating-human-like-few-shot-learning","title":"Approximating Human-Like Few-shot Learning with GPT-based Compression","date":"2023-08-14","arxiv_id":"2308.06942","n_code_links":0,"syntology":null},{"paper":null,"slug":"chatbots-in-drug-discovery-a-case-study-on","title":"ChatGPT in Drug Discovery: A Case Study on Anti-Cocaine Addiction Drug Development with Chatbots","date":"2023-08-14","arxiv_id":"2308.06920","n_code_links":0,"syntology":null},{"paper":"/paper/dialogue-for-prompting-a-policy-gradient","slug":"dialogue-for-prompting-a-policy-gradient","title":"Dialogue for Prompting: a Policy-Gradient-Based Discrete Prompt Generation for Few-shot Learning","date":"2023-08-14","arxiv_id":"2308.07272","n_code_links":1,"syntology":{"ran":3,"of":4,"n_ran_checked":3,"n_instrument":0,"unverified":1,"pointer_only":1,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","official":{"repos":["czx-li/DP2O"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"diffsed-sound-event-detection-with-denoising","title":"DiffSED: Sound Event Detection with Denoising Diffusion","date":"2023-08-14","arxiv_id":"2308.07293","n_code_links":0,"syntology":null},{"paper":null,"slug":"how-inter-rater-variability-relates-to","title":"How inter-rater variability relates to aleatoric and epistemic uncertainty: a case study with deep learning-based paraspinal muscle segmentation","date":"2023-08-14","arxiv_id":"2308.06964","n_code_links":0,"syntology":null},{"paper":"/paper/large-language-models-for-information","slug":"large-language-models-for-information","title":"Large Language Models for Information Retrieval: A Survey","date":"2023-08-14","arxiv_id":"2308.07107","n_code_links":1,"syntology":null},{"paper":"/paper/neural-authorship-attribution-stylometric","slug":"neural-authorship-attribution-stylometric","title":"Neural Authorship Attribution: Stylometric Analysis on Large Language Models","date":"2023-08-14","arxiv_id":"2308.07305","n_code_links":1,"syntology":null},{"paper":null,"slug":"on-the-importance-of-spatial-relations-for","title":"On the Importance of Spatial Relations for Few-shot Action Recognition","date":"2023-08-14","arxiv_id":"2308.07119","n_code_links":0,"syntology":null},{"paper":"/paper/pairing-interacting-protein-sequences-using","slug":"pairing-interacting-protein-sequences-using","title":"Pairing interacting protein sequences using masked language modeling","date":"2023-08-14","arxiv_id":"2308.07136","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":["bitbol-lab/diffpalm"],"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":"scsc-spatial-cross-scale-convolution-module","title":"SCSC: Spatial Cross-scale Convolution Module to Strengthen both CNNs and Transformers","date":"2023-08-14","arxiv_id":"2308.07110","n_code_links":0,"syntology":null},{"paper":null,"slug":"faithful-to-whom-questioning-interpretability","title":"Robust Infidelity: When Faithfulness Measures on Masked Language Models Are Misleading","date":"2023-08-13","arxiv_id":"2308.06795","n_code_links":0,"syntology":null},{"paper":"/paper/isomer-isomerous-transformer-for-zero-shot","slug":"isomer-isomerous-transformer-for-zero-shot","title":"Isomer: Isomerous Transformer for Zero-shot Video Object Segmentation","date":"2023-08-13","arxiv_id":"2308.06693","n_code_links":1,"syntology":null},{"paper":null,"slug":"modified-topological-image-preprocessing-for","title":"Modified Topological Image Preprocessing for Skin Lesion Classifications","date":"2023-08-13","arxiv_id":"2308.06796","n_code_links":0,"syntology":null},{"paper":"/paper/3dmotformer-graph-transformer-for-online-3d","slug":"3dmotformer-graph-transformer-for-online-3d","title":"3DMOTFormer: Graph Transformer for Online 3D Multi-Object Tracking","date":"2023-08-12","arxiv_id":"2308.06635","n_code_links":1,"syntology":null},{"paper":"/paper/gpt-4-is-too-smart-to-be-safe-stealthy-chat","slug":"gpt-4-is-too-smart-to-be-safe-stealthy-chat","title":"GPT-4 Is Too Smart To Be Safe: Stealthy Chat with LLMs via Cipher","date":"2023-08-12","arxiv_id":"2308.06463","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":["robustnlp/cipherchat"],"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":null,"slug":"performance-analysis-for-resource-constrained","title":"Performance Analysis for Resource Constrained Decentralized Federated Learning Over Wireless Networks","date":"2023-08-12","arxiv_id":"2308.06496","n_code_links":0,"syntology":null},{"paper":"/paper/visit-bench-a-benchmark-for-vision-language","slug":"visit-bench-a-benchmark-for-vision-language","title":"VisIT-Bench: A Benchmark for Vision-Language Instruction Following Inspired by Real-World Use","date":"2023-08-12","arxiv_id":"2308.06595","n_code_links":1,"syntology":{"ran":9,"of":9,"n_ran_checked":3,"n_instrument":6,"unverified":0,"pointer_only":9,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 1 violated, 2 with no contract checked; 6 where Syntology's instrument failed) · 0 unverified","official":{"repos":["mlfoundations/VisIT-Bench"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"assessing-student-errors-in-experimentation","title":"Assessing Student Errors in Experimentation Using Artificial Intelligence and Large Language Models: A Comparative Study with Human Raters","date":"2023-08-11","arxiv_id":"2308.06088","n_code_links":0,"syntology":null},{"paper":null,"slug":"chatgpt-based-investment-portfolio-selection","title":"ChatGPT-based Investment Portfolio Selection","date":"2023-08-11","arxiv_id":"2308.06260","n_code_links":0,"syntology":null},{"paper":"/paper/exploring-predicate-visual-context-in","slug":"exploring-predicate-visual-context-in","title":"Exploring Predicate Visual Context in Detecting Human-Object Interactions","date":"2023-08-11","arxiv_id":"2308.06202","n_code_links":1,"syntology":{"ran":6,"of":9,"n_ran_checked":5,"n_instrument":1,"unverified":3,"pointer_only":1,"phrase":"6 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; 1 where Syntology's instrument failed) · 3 unverified","official":{"repos":["fredzzhang/pvic"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":3,"ran_from_kinds":["official"]}}},{"paper":"/paper/learning-deductive-reasoning-from-synthetic","slug":"learning-deductive-reasoning-from-synthetic","title":"Learning Deductive Reasoning from Synthetic Corpus based on Formal Logic","date":"2023-08-11","arxiv_id":"2308.07336","n_code_links":3,"syntology":{"ran":1,"of":2,"n_ran_checked":1,"n_instrument":0,"unverified":1,"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) · 1 unverified","official":{"repos":["hitachi-nlp/fld"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":"/paper/revolutionizing-space-health-swin-fsr","slug":"revolutionizing-space-health-swin-fsr","title":"Revolutionizing Space Health (Swin-FSR): Advancing Super-Resolution of Fundus Images for SANS Visual Assessment Technology","date":"2023-08-11","arxiv_id":"2308.06332","n_code_links":1,"syntology":null},{"paper":null,"slug":"task-conditioned-bert-for-joint-intent","title":"Task Conditioned BERT for Joint Intent Detection and Slot-filling","date":"2023-08-11","arxiv_id":"2308.06165","n_code_links":0,"syntology":null},{"paper":null,"slug":"vigt-proposal-free-video-grounding-with","title":"ViGT: Proposal-free Video Grounding with Learnable Token in Transformer","date":"2023-08-11","arxiv_id":"2308.06009","n_code_links":0,"syntology":null},{"paper":"/paper/adaptive-low-rank-adaptation-of-segment","slug":"adaptive-low-rank-adaptation-of-segment","title":"Adaptive Low Rank Adaptation of Segment Anything to Salient Object Detection","date":"2023-08-10","arxiv_id":"2308.05426","n_code_links":1,"syntology":null},{"paper":null,"slug":"bringing-order-into-the-realm-of-transformer","title":"Bringing order into the realm of Transformer-based language models for artificial intelligence and law","date":"2023-08-10","arxiv_id":"2308.05502","n_code_links":0,"syntology":null},{"paper":"/paper/category-feature-transformer-for-semantic","slug":"category-feature-transformer-for-semantic","title":"Category Feature Transformer for Semantic Segmentation","date":"2023-08-10","arxiv_id":"2308.05581","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":["BebDong/EMOSeg"],"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":"deformable-mixer-transformer-with-gating-for","title":"Deformable Mixer Transformer with Gating for Multi-Task Learning of Dense Prediction","date":"2023-08-10","arxiv_id":"2308.05721","n_code_links":0,"syntology":null},{"paper":"/paper/double-chain-constraints-for-3d-human-pose","slug":"double-chain-constraints-for-3d-human-pose","title":"Double-chain Constraints for 3D Human Pose Estimation in Images and Videos","date":"2023-08-10","arxiv_id":"2308.05298","n_code_links":1,"syntology":null},{"paper":null,"slug":"finding-already-debunked-narratives-via","title":"Breaking Language Barriers with MMTweets: Advancing Cross-Lingual Debunked Narrative Retrieval for Fact-Checking","date":"2023-08-10","arxiv_id":"2308.05680","n_code_links":0,"syntology":null},{"paper":null,"slug":"global-in-local-a-convolutional-transformer","title":"Global in Local: A Convolutional Transformer for SAR ATR FSL","date":"2023-08-10","arxiv_id":"2308.05464","n_code_links":0,"syntology":null},{"paper":null,"slug":"informative-scene-graph-generation-via","title":"Informative Scene Graph Generation via Debiasing","date":"2023-08-10","arxiv_id":"2308.05286","n_code_links":0,"syntology":null},{"paper":"/paper/interaction-aware-joint-attention-estimation","slug":"interaction-aware-joint-attention-estimation","title":"Interaction-aware Joint Attention Estimation Using People Attributes","date":"2023-08-10","arxiv_id":"2308.05382","n_code_links":1,"syntology":null},{"paper":"/paper/metacognitive-prompting-improves","slug":"metacognitive-prompting-improves","title":"Metacognitive Prompting Improves Understanding in Large Language Models","date":"2023-08-10","arxiv_id":"2308.05342","n_code_links":1,"syntology":null},{"paper":"/paper/temporally-adaptive-models-for-efficient","slug":"temporally-adaptive-models-for-efficient","title":"Temporally-Adaptive Models for Efficient Video Understanding","date":"2023-08-10","arxiv_id":"2308.05787","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":["alibaba-mmai-research/TAdaConv"],"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":"testing-gpt-4-with-wolfram-alpha-and-code","title":"Testing GPT-4 with Wolfram Alpha and Code Interpreter plug-ins on math and science problems","date":"2023-08-10","arxiv_id":"2308.05713","n_code_links":0,"syntology":null},{"paper":"/paper/trustworthy-llms-a-survey-and-guideline-for","slug":"trustworthy-llms-a-survey-and-guideline-for","title":"Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' Alignment","date":"2023-08-10","arxiv_id":"2308.05374","n_code_links":1,"syntology":null},{"paper":"/paper/a-bipartite-graph-is-all-we-need-for","slug":"a-bipartite-graph-is-all-we-need-for","title":"A Bipartite Graph is All We Need for Enhancing Emotional Reasoning with Commonsense Knowledge","date":"2023-08-09","arxiv_id":"2308.04811","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-comparative-study-of-open-source-large","title":"A Comparative Study of Open-Source Large Language Models, GPT-4 and Claude 2: Multiple-Choice Test Taking in Nephrology","date":"2023-08-09","arxiv_id":"2308.04709","n_code_links":0,"syntology":null},{"paper":"/paper/joint-relation-transformer-for-multi-person","slug":"joint-relation-transformer-for-multi-person","title":"Joint-Relation Transformer for Multi-Person Motion Prediction","date":"2023-08-09","arxiv_id":"2308.04808","n_code_links":1,"syntology":{"ran":18,"of":21,"n_ran_checked":17,"n_instrument":1,"unverified":3,"pointer_only":21,"phrase":"18 ran (of which 0 constructed an object rather than computing a result; 17 with no instrument failure: 0 honoured, 0 violated, 17 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","official":{"repos":["mediabrain-sjtu/jrtransformer"],"state":"official (archive's flag): 18 ran","n_ran":18,"n_constructed":0,"n_ran_no_instrument_failure":17,"n_unverified":3,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"optimizing-a-transformer-based-network-for-a","title":"Optimizing a Transformer-based network for a deep learning seismic processing workflow","date":"2023-08-09","arxiv_id":"2308.04739","n_code_links":0,"syntology":null},{"paper":"/paper/petformer-long-term-time-series-forecasting","slug":"petformer-long-term-time-series-forecasting","title":"PETformer: Long-term Time Series Forecasting via Placeholder-enhanced Transformer","date":"2023-08-09","arxiv_id":"2308.04791","n_code_links":1,"syntology":{"ran":2,"of":3,"n_ran_checked":2,"n_instrument":0,"unverified":1,"pointer_only":3,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","official":{"repos":["ACAT-SCUT/PETformer"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":"/paper/robust-object-modeling-for-visual-tracking","slug":"robust-object-modeling-for-visual-tracking","title":"Robust Object Modeling for Visual Tracking","date":"2023-08-09","arxiv_id":"2308.05140","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: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["dawnyc/romtrack"],"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":"sparse-binary-transformers-for-multivariate","title":"Sparse Binary Transformers for Multivariate Time Series Modeling","date":"2023-08-09","arxiv_id":"2308.04637","n_code_links":0,"syntology":null},{"paper":"/paper/which-tokens-to-use-investigating-token","slug":"which-tokens-to-use-investigating-token","title":"Which Tokens to Use? Investigating Token Reduction in Vision Transformers","date":"2023-08-09","arxiv_id":"2308.04657","n_code_links":1,"syntology":{"ran":8,"of":8,"n_ran_checked":6,"n_instrument":2,"unverified":0,"pointer_only":3,"phrase":"8 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; 2 where Syntology's instrument failed) · 0 unverified","official":null}},{"paper":"/paper/3d-vista-pre-trained-transformer-for-3d","slug":"3d-vista-pre-trained-transformer-for-3d","title":"3D-VisTA: Pre-trained Transformer for 3D Vision and Text Alignment","date":"2023-08-08","arxiv_id":"2308.04352","n_code_links":1,"syntology":{"ran":4,"of":6,"n_ran_checked":4,"n_instrument":0,"unverified":2,"pointer_only":0,"phrase":"4 ran (of which 2 constructed an object rather than computing a result; 4 with no instrument failure: 1 honoured, 1 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","official":null}},{"paper":null,"slug":"character-level-nmt-and-language-similarity","title":"Character-level NMT and language similarity","date":"2023-08-08","arxiv_id":"2308.04398","n_code_links":0,"syntology":null},{"paper":null,"slug":"chatgpt-for-arabic-grammatical-error","title":"ChatGPT for Arabic Grammatical Error Correction","date":"2023-08-08","arxiv_id":"2308.04492","n_code_links":0,"syntology":null},{"paper":null,"slug":"comparing-color-similarity-structures-between","title":"Gromov-Wasserstein unsupervised alignment reveals structural correspondences between the color similarity structures of humans and large language models","date":"2023-08-08","arxiv_id":"2308.04381","n_code_links":0,"syntology":null},{"paper":"/paper/epcformer-expression-prompt-collaboration","slug":"epcformer-expression-prompt-collaboration","title":"Expression Prompt Collaboration Transformer for Universal Referring Video Object Segmentation","date":"2023-08-08","arxiv_id":"2308.04162","n_code_links":0,"syntology":null},{"paper":null,"slug":"exploring-transformers-for-open-world","title":"Exploring Transformers for Open-world Instance Segmentation","date":"2023-08-08","arxiv_id":"2308.04206","n_code_links":0,"syntology":null},{"paper":null,"slug":"few-shot-medical-image-classification-with","title":"Few-shot medical image classification with simple shape and texture text descriptors using vision-language models","date":"2023-08-08","arxiv_id":"2308.04005","n_code_links":0,"syntology":null},{"paper":"/paper/hierarchical-visual-primitive-experts-for","slug":"hierarchical-visual-primitive-experts-for","title":"Hierarchical Visual Primitive Experts for Compositional Zero-Shot Learning","date":"2023-08-08","arxiv_id":"2308.04016","n_code_links":1,"syntology":{"ran":11,"of":12,"n_ran_checked":8,"n_instrument":3,"unverified":1,"pointer_only":12,"phrase":"11 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; 3 where Syntology's instrument failed) · 1 unverified","official":{"repos":["hanjaekim98/cot"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":"/paper/leformer-a-hybrid-cnn-transformer","slug":"leformer-a-hybrid-cnn-transformer","title":"LEFormer: A Hybrid CNN-Transformer Architecture for Accurate Lake Extraction from Remote Sensing Imagery","date":"2023-08-08","arxiv_id":"2308.04397","n_code_links":1,"syntology":null},{"paper":"/paper/multiscale-patch-based-feature-graphs-for","slug":"multiscale-patch-based-feature-graphs-for","title":"Multiscale patch-based feature graphs for image classification","date":"2023-08-08","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/online-distillation-enhanced-multi-modal","slug":"online-distillation-enhanced-multi-modal","title":"Online Distillation-enhanced Multi-modal Transformer for Sequential Recommendation","date":"2023-08-08","arxiv_id":"2308.04067","n_code_links":1,"syntology":null},{"paper":"/paper/ptransips-identification-of-phosphorylation","slug":"ptransips-identification-of-phosphorylation","title":"PTransIPs: Identification of phosphorylation sites enhanced by protein PLM embeddings","date":"2023-08-08","arxiv_id":"2308.05115","n_code_links":1,"syntology":null},{"paper":"/paper/shepherd-a-critic-for-language-model","slug":"shepherd-a-critic-for-language-model","title":"Shepherd: A Critic for Language Model Generation","date":"2023-08-08","arxiv_id":"2308.04592","n_code_links":1,"syntology":null},{"paper":"/paper/sodformer-streaming-object-detection-with","slug":"sodformer-streaming-object-detection-with","title":"SODFormer: Streaming Object Detection with Transformer Using Events and Frames","date":"2023-08-08","arxiv_id":"2308.04047","n_code_links":1,"syntology":null},{"paper":"/paper/sstformer-bridging-spiking-neural-network-and","slug":"sstformer-bridging-spiking-neural-network-and","title":"SSTFormer: Bridging Spiking Neural Network and Memory Support Transformer for Frame-Event based Recognition","date":"2023-08-08","arxiv_id":"2308.04369","n_code_links":1,"syntology":null},{"paper":null,"slug":"temporal-dino-a-self-supervised-video","title":"Temporal DINO: A Self-supervised Video Strategy to Enhance Action Prediction","date":"2023-08-08","arxiv_id":"2308.04589","n_code_links":0,"syntology":null},{"paper":"/paper/unifying-two-stream-encoders-with","slug":"unifying-two-stream-encoders-with","title":"Unifying Two-Stream Encoders with Transformers for Cross-Modal Retrieval","date":"2023-08-08","arxiv_id":"2308.04343","n_code_links":1,"syntology":null},{"paper":"/paper/v-detr-detr-with-vertex-relative-position","slug":"v-detr-detr-with-vertex-relative-position","title":"V-DETR: DETR with Vertex Relative Position Encoding for 3D Object Detection","date":"2023-08-08","arxiv_id":"2308.04409","n_code_links":1,"syntology":{"ran":10,"of":12,"n_ran_checked":7,"n_instrument":3,"unverified":2,"pointer_only":2,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 3 where Syntology's instrument failed) · 2 unverified","official":{"repos":["yichaoshen-ms/v-detr"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":2,"ran_from_kinds":["official"]}}}],"record_sha256":"0e98e9c6a255c61735337aa685804656e6bfcf59e0ae14d4cb831d4b48376a8d","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}