{"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/softmax/papers/250","list_of":"/method/softmax","method":"Softmax","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":250,"pages_in_order":375,"rows_per_page":100,"rows":[24901,25000],"of":37443,"counts":{"archive_papers_tagged":37443,"with_a_code_link":15869,"where_syntology_ran_a_sample":4578,"not_listed_spam_title":0,"listed":37443,"listed_where_code_ran":4578,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":3835,"every_run_a_failure_of_syntologys_instrument":743,"listed_with_a_run_with_no_instrument_failure":3835,"listed_every_run_a_failure_of_syntologys_instrument":743,"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/softmax","prev":"/method/softmax/papers/249","next":"/method/softmax/papers/251","papers":[{"paper":null,"slug":"interpreting-bert-based-text-similarity-via","title":"Interpreting BERT-based Text Similarity via Activation and Saliency Maps","date":"2022-08-13","arxiv_id":"2208.06612","n_code_links":0,"syntology":null},{"paper":null,"slug":"an-algorithm-hardware-co-optimized-framework","title":"An Algorithm-Hardware Co-Optimized Framework for Accelerating N:M Sparse Transformers","date":"2022-08-12","arxiv_id":"2208.06118","n_code_links":0,"syntology":null},{"paper":"/paper/beit-v2-masked-image-modeling-with-vector","slug":"beit-v2-masked-image-modeling-with-vector","title":"BEiT v2: Masked Image Modeling with Vector-Quantized Visual Tokenizers","date":"2022-08-12","arxiv_id":"2208.06366","n_code_links":3,"syntology":null},{"paper":"/paper/class-attention-video-transformer-for","slug":"class-attention-video-transformer-for","title":"Class-attention Video Transformer for Engagement Intensity Prediction","date":"2022-08-12","arxiv_id":"2208.07216","n_code_links":1,"syntology":null},{"paper":null,"slug":"is-your-model-sensitive-spedac-a-new","title":"Is Your Model Sensitive? SPeDaC: A New Benchmark for Detecting and Classifying Sensitive Personal Data","date":"2022-08-12","arxiv_id":"2208.06216","n_code_links":0,"syntology":null},{"paper":null,"slug":"layout-bridging-text-to-image-synthesis","title":"Layout-Bridging Text-to-Image Synthesis","date":"2022-08-12","arxiv_id":"2208.06162","n_code_links":0,"syntology":null},{"paper":"/paper/pre-training-tasks-for-user-intent-detection","slug":"pre-training-tasks-for-user-intent-detection","title":"Pre-training Tasks for User Intent Detection and Embedding Retrieval in E-commerce Search","date":"2022-08-12","arxiv_id":"2208.06150","n_code_links":1,"syntology":null},{"paper":"/paper/real-time-accident-detection-in-traffic","slug":"real-time-accident-detection-in-traffic","title":"Real-Time Accident Detection in Traffic Surveillance Using Deep Learning","date":"2022-08-12","arxiv_id":"2208.06461","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-model-of-anaphoric-ambiguities-using-sheaf","title":"A Model of Anaphoric Ambiguities using Sheaf Theoretic Quantum-like Contextuality and BERT","date":"2022-08-11","arxiv_id":"2208.05720","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-twitter-driven-deep-learning-mechanism-for","title":"A Twitter-Driven Deep Learning Mechanism for the Determination of Vehicle Hijacking Spots in Cities","date":"2022-08-11","arxiv_id":"2208.10280","n_code_links":0,"syntology":null},{"paper":null,"slug":"armani-part-level-garment-text-alignment-for","title":"ARMANI: Part-level Garment-Text Alignment for Unified Cross-Modal Fashion Design","date":"2022-08-11","arxiv_id":"2208.05621","n_code_links":0,"syntology":null},{"paper":"/paper/deep-is-a-luxury-we-don-t-have","slug":"deep-is-a-luxury-we-don-t-have","title":"Deep is a Luxury We Don't Have","date":"2022-08-11","arxiv_id":"2208.06066","n_code_links":1,"syntology":null},{"paper":"/paper/domain-specific-text-generation-for-machine","slug":"domain-specific-text-generation-for-machine","title":"Domain-Specific Text Generation for Machine Translation","date":"2022-08-11","arxiv_id":"2208.05909","n_code_links":2,"syntology":null},{"paper":null,"slug":"learning-point-processes-using-recurrent","title":"Learning Point Processes using Recurrent Graph Network","date":"2022-08-11","arxiv_id":"2208.05736","n_code_links":0,"syntology":null},{"paper":null,"slug":"new-drugs-and-stock-market-how-to-predict","title":"New drugs and stock market: how to predict pharma market reaction to clinical trial announcements","date":"2022-08-11","arxiv_id":"2208.07248","n_code_links":0,"syntology":null},{"paper":null,"slug":"searching-for-chromate-replacements-using","title":"Searching for chromate replacements using natural language processing and machine learning algorithms","date":"2022-08-11","arxiv_id":"2208.05672","n_code_links":0,"syntology":null},{"paper":"/paper/semi-supervised-vision-transformers-at-scale","slug":"semi-supervised-vision-transformers-at-scale","title":"Semi-supervised Vision Transformers at Scale","date":"2022-08-11","arxiv_id":"2208.05688","n_code_links":1,"syntology":null},{"paper":null,"slug":"shifted-windows-transformers-for-medical","title":"Shifted Windows Transformers for Medical Image Quality Assessment","date":"2022-08-11","arxiv_id":"2208.06034","n_code_links":0,"syntology":null},{"paper":null,"slug":"solving-mathword-problems-automatically-with","title":"Heterogeneous Line Graph Transformer for Math Word Problems","date":"2022-08-11","arxiv_id":"2208.05645","n_code_links":0,"syntology":null},{"paper":null,"slug":"structural-biases-for-improving-transformers-1","title":"Structural Biases for Improving Transformers on Translation into Morphologically Rich Languages","date":"2022-08-11","arxiv_id":"2208.06061","n_code_links":0,"syntology":null},{"paper":"/paper/an-empirical-exploration-of-cross-domain","slug":"an-empirical-exploration-of-cross-domain","title":"Can Brain Signals Reveal Inner Alignment with Human Languages?","date":"2022-08-10","arxiv_id":"2208.06348","n_code_links":1,"syntology":null},{"paper":"/paper/arbitrary-point-cloud-upsampling-with","slug":"arbitrary-point-cloud-upsampling-with","title":"Arbitrary Point Cloud Upsampling with Spherical Mixture of Gaussians","date":"2022-08-10","arxiv_id":"2208.05274","n_code_links":1,"syntology":null},{"paper":"/paper/exploring-point-bev-fusion-for-3d-point-cloud","slug":"exploring-point-bev-fusion-for-3d-point-cloud","title":"Exploring Point-BEV Fusion for 3D Point Cloud Object Tracking with Transformer","date":"2022-08-10","arxiv_id":"2208.05216","n_code_links":1,"syntology":null},{"paper":"/paper/generative-transfer-learning-covid-19","slug":"generative-transfer-learning-covid-19","title":"Generative Transfer Learning: Covid-19 Classification with a few Chest X-ray Images","date":"2022-08-10","arxiv_id":"2208.05305","n_code_links":1,"syntology":null},{"paper":"/paper/ghost-free-high-dynamic-range-imaging-with","slug":"ghost-free-high-dynamic-range-imaging-with","title":"Ghost-free High Dynamic Range Imaging with Context-aware Transformer","date":"2022-08-10","arxiv_id":"2208.05114","n_code_links":3,"syntology":{"ran":5,"of":8,"n_ran_checked":5,"n_instrument":0,"unverified":3,"pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","official":{"repos":["megvii-research/hdr-transformer"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":3,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"multi-scale-feature-aggregation-for-crowd","title":"Multi-scale Feature Aggregation for Crowd Counting","date":"2022-08-10","arxiv_id":"2208.05256","n_code_links":0,"syntology":null},{"paper":null,"slug":"non-contrastive-self-supervised-learning-for","title":"Non-Contrastive Self-supervised Learning for Utterance-Level Information Extraction from Speech","date":"2022-08-10","arxiv_id":"2208.05445","n_code_links":0,"syntology":null},{"paper":"/paper/non-contrastive-self-supervised-learning-of","slug":"non-contrastive-self-supervised-learning-of","title":"Non-Contrastive Self-Supervised Learning of Utterance-Level Speech Representations","date":"2022-08-10","arxiv_id":"2208.05413","n_code_links":1,"syntology":null},{"paper":null,"slug":"towards-daily-high-resolution-inundation","title":"Towards Daily High-resolution Inundation Observations using Deep Learning and EO","date":"2022-08-10","arxiv_id":"2208.09135","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-boring-yet-effective-approach-for-the","title":"A Boring-yet-effective Approach for the Product Ranking Task of the Amazon KDD Cup 2022","date":"2022-08-09","arxiv_id":"2208.06264","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-multimodal-transformer-fusing-clinical","title":"A Multimodal Transformer: Fusing Clinical Notes with Structured EHR Data for Interpretable In-Hospital Mortality Prediction","date":"2022-08-09","arxiv_id":"2208.10240","n_code_links":0,"syntology":null},{"paper":null,"slug":"attention-hijacking-in-trojan-transformers","title":"Attention Hijacking in Trojan Transformers","date":"2022-08-09","arxiv_id":"2208.04946","n_code_links":0,"syntology":null},{"paper":"/paper/covit-real-time-phylogenetics-for-the-sars","slug":"covit-real-time-phylogenetics-for-the-sars","title":"CoViT: Real-time phylogenetics for the SARS-CoV-2 pandemic using Vision Transformers","date":"2022-08-09","arxiv_id":"2208.05004","n_code_links":1,"syntology":null},{"paper":"/paper/e2eg-end-to-end-node-classification-using","slug":"e2eg-end-to-end-node-classification-using","title":"E2EG: End-to-End Node Classification Using Graph Topology and Text-based Node Attributes","date":"2022-08-09","arxiv_id":"2208.04609","n_code_links":1,"syntology":{"ran":5,"of":9,"n_ran_checked":3,"n_instrument":2,"unverified":4,"pointer_only":4,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 1 honoured, 0 violated, 2 with no contract checked; 2 where Syntology's instrument failed) · 4 unverified","official":{"repos":["tuanh23/e2eg"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":4,"ran_from_kinds":["official"]}}},{"paper":"/paper/emotion-detection-from-tweets-using-a-bert","slug":"emotion-detection-from-tweets-using-a-bert","title":"Emotion Detection From Tweets Using a BERT and SVM Ensemble Model","date":"2022-08-09","arxiv_id":"2208.04547","n_code_links":1,"syntology":null},{"paper":"/paper/exploring-hate-speech-detection-with","slug":"exploring-hate-speech-detection-with","title":"Exploring Hate Speech Detection with HateXplain and BERT","date":"2022-08-09","arxiv_id":"2208.04489","n_code_links":1,"syntology":null},{"paper":null,"slug":"high-recall-data-to-text-generation-with","title":"High Recall Data-to-text Generation with Progressive Edit","date":"2022-08-09","arxiv_id":"2208.04558","n_code_links":0,"syntology":null},{"paper":null,"slug":"res-dense-net-for-3d-covid-chest-ct-scan","title":"Res-Dense Net for 3D Covid Chest CT-scan classification","date":"2022-08-09","arxiv_id":"2208.04613","n_code_links":0,"syntology":null},{"paper":"/paper/tsrformer-table-structure-recognition-with","slug":"tsrformer-table-structure-recognition-with","title":"TSRFormer: Table Structure Recognition with Transformers","date":"2022-08-09","arxiv_id":"2208.04921","n_code_links":0,"syntology":null},{"paper":null,"slug":"debiased-large-language-models-still","title":"Debiased Large Language Models Still Associate Muslims with Uniquely Violent Acts","date":"2022-08-08","arxiv_id":"2208.04417","n_code_links":0,"syntology":null},{"paper":"/paper/generating-coherent-narratives-by-learning","slug":"generating-coherent-narratives-by-learning","title":"Generating Coherent Narratives by Learning Dynamic and Discrete Entity States with a Contrastive Framework","date":"2022-08-08","arxiv_id":"2208.03985","n_code_links":1,"syntology":null},{"paper":"/paper/investigating-efficiently-extending","slug":"investigating-efficiently-extending","title":"Investigating Efficiently Extending Transformers for Long Input Summarization","date":"2022-08-08","arxiv_id":"2208.04347","n_code_links":2,"syntology":null},{"paper":null,"slug":"object-detection-using-sim2real-domain","title":"Object Detection Using Sim2Real Domain Randomization for Robotic Applications","date":"2022-08-08","arxiv_id":"2208.04171","n_code_links":0,"syntology":null},{"paper":"/paper/occlusion-aware-instance-segmentation-via","slug":"occlusion-aware-instance-segmentation-via","title":"Occlusion-Aware Instance Segmentation via BiLayer Network Architectures","date":"2022-08-08","arxiv_id":"2208.04438","n_code_links":1,"syntology":null},{"paper":"/paper/skdcgn-source-free-knowledge-distillation-of","slug":"skdcgn-source-free-knowledge-distillation-of","title":"SKDCGN: Source-free Knowledge Distillation of Counterfactual Generative Networks using cGANs","date":"2022-08-08","arxiv_id":"2208.04226","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-length-adaptive-algorithm-hardware-co","title":"A Length Adaptive Algorithm-Hardware Co-design of Transformer on FPGA Through Sparse Attention and Dynamic Pipelining","date":"2022-08-07","arxiv_id":"2208.03646","n_code_links":0,"syntology":null},{"paper":"/paper/cross-skeleton-interaction-graph-aggregation","slug":"cross-skeleton-interaction-graph-aggregation","title":"Cross-Skeleton Interaction Graph Aggregation Network for Representation Learning of Mouse Social Behaviour","date":"2022-08-07","arxiv_id":"2208.03819","n_code_links":1,"syntology":null},{"paper":null,"slug":"analysing-the-memorability-of-a-procedural","title":"Analysing the Memorability of a Procedural Crime-Drama TV Series, CSI","date":"2022-08-06","arxiv_id":"2208.03479","n_code_links":0,"syntology":null},{"paper":"/paper/frozen-clip-models-are-efficient-video","slug":"frozen-clip-models-are-efficient-video","title":"Frozen CLIP Models are Efficient Video Learners","date":"2022-08-06","arxiv_id":"2208.03550","n_code_links":2,"syntology":{"ran":5,"of":10,"n_ran_checked":5,"n_instrument":0,"unverified":5,"pointer_only":10,"phrase":"5 ran (of which 3 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 5 unverified","official":{"repos":["opengvlab/efficient-video-recognition"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":3,"n_ran_no_instrument_failure":5,"n_unverified":5,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"haloae-an-halonet-based-local-transformer","title":"HaloAE: An HaloNet based Local Transformer Auto-Encoder for Anomaly Detection and Localization","date":"2022-08-06","arxiv_id":"2208.03486","n_code_links":0,"syntology":null},{"paper":"/paper/hsic-infogan-learning-unsupervised","slug":"hsic-infogan-learning-unsupervised","title":"HSIC-InfoGAN: Learning Unsupervised Disentangled Representations by Maximising Approximated Mutual Information","date":"2022-08-06","arxiv_id":"2208.03563","n_code_links":1,"syntology":null},{"paper":null,"slug":"inpainting-at-modern-camera-resolution-by","title":"Inpainting at Modern Camera Resolution by Guided PatchMatch with Auto-Curation","date":"2022-08-06","arxiv_id":"2208.03552","n_code_links":0,"syntology":null},{"paper":"/paper/ivt-an-end-to-end-instance-guided-video","slug":"ivt-an-end-to-end-instance-guided-video","title":"IVT: An End-to-End Instance-guided Video Transformer for 3D Pose Estimation","date":"2022-08-06","arxiv_id":"2208.03431","n_code_links":0,"syntology":null},{"paper":"/paper/monovit-self-supervised-monocular-depth","slug":"monovit-self-supervised-monocular-depth","title":"MonoViT: Self-Supervised Monocular Depth Estimation with a Vision Transformer","date":"2022-08-06","arxiv_id":"2208.03543","n_code_links":1,"syntology":{"ran":6,"of":6,"n_ran_checked":4,"n_instrument":2,"unverified":0,"pointer_only":4,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 1 honoured, 0 violated, 3 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","official":{"repos":["zxcqlf/monovit"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"ssdpt-self-supervised-dual-path-transformer","title":"SSDPT: Self-Supervised Dual-Path Transformer for Anomalous Sound Detection in Machine Condition Monitoring","date":"2022-08-06","arxiv_id":"2208.03421","n_code_links":0,"syntology":null},{"paper":"/paper/a-spatially-separable-attention-mechanism-for","slug":"a-spatially-separable-attention-mechanism-for","title":"A Spatially Separable Attention Mechanism for massive MIMO CSI Feedback","date":"2022-08-05","arxiv_id":"2208.03369","n_code_links":1,"syntology":null},{"paper":null,"slug":"brainformer-a-hybrid-cnn-transformer-model","title":"Multimodal Brain Disease Classification with Functional Interaction Learning from Single fMRI Volume","date":"2022-08-05","arxiv_id":"2208.03028","n_code_links":0,"syntology":null},{"paper":"/paper/branch-train-merge-embarrassingly-parallel","slug":"branch-train-merge-embarrassingly-parallel","title":"Branch-Train-Merge: Embarrassingly Parallel Training of Expert Language Models","date":"2022-08-05","arxiv_id":"2208.03306","n_code_links":2,"syntology":{"ran":1,"of":1,"n_ran_checked":0,"n_instrument":1,"unverified":0,"pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["hadasah/btm"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/croloss-towards-a-customizable-loss-for","slug":"croloss-towards-a-customizable-loss-for","title":"CROLoss: Towards a Customizable Loss for Retrieval Models in Recommender Systems","date":"2022-08-05","arxiv_id":"2208.02971","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":["WDdeBWT/CROLoss"],"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":"deep-learning-neural-network-for-lung-cancer","title":"Deep Learning Neural Network for Lung Cancer Classification: Enhanced Optimization Function","date":"2022-08-05","arxiv_id":"2208.06353","n_code_links":0,"syntology":null},{"paper":"/paper/deepwsd-projecting-degradations-in-perceptual","slug":"deepwsd-projecting-degradations-in-perceptual","title":"DeepWSD: Projecting Degradations in Perceptual Space to Wasserstein Distance in Deep Feature Space","date":"2022-08-05","arxiv_id":"2208.03323","n_code_links":1,"syntology":null},{"paper":null,"slug":"global-pointer-novel-efficient-span-based","title":"Global Pointer: Novel Efficient Span-based Approach for Named Entity Recognition","date":"2022-08-05","arxiv_id":"2208.03054","n_code_links":0,"syntology":null},{"paper":null,"slug":"acsgregnet-a-deep-learning-based-framework","title":"ACSGRegNet: A Deep Learning-based Framework for Unsupervised Joint Affine and Diffeomorphic Registration of Lumbar Spine CT via Cross- and Self-Attention Fusion","date":"2022-08-04","arxiv_id":"2208.02642","n_code_links":0,"syntology":null},{"paper":null,"slug":"dropkey","title":"DropKey","date":"2022-08-04","arxiv_id":"2208.02646","n_code_links":0,"syntology":null},{"paper":"/paper/self-ensembling-vision-transformer-sevit-for","slug":"self-ensembling-vision-transformer-sevit-for","title":"Self-Ensembling Vision Transformer (SEViT) for Robust Medical Image Classification","date":"2022-08-04","arxiv_id":"2208.02851","n_code_links":1,"syntology":null},{"paper":"/paper/transformers-as-meta-learners-for-implicit","slug":"transformers-as-meta-learners-for-implicit","title":"Transformers as Meta-Learners for Implicit Neural Representations","date":"2022-08-04","arxiv_id":"2208.02801","n_code_links":1,"syntology":{"ran":1,"of":2,"n_ran_checked":0,"n_instrument":1,"unverified":1,"pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","official":{"repos":["yinboc/trans-inr"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":"/paper/utopic-uncertainty-aware-overlap-prediction","slug":"utopic-uncertainty-aware-overlap-prediction","title":"UTOPIC: Uncertainty-aware Overlap Prediction Network for Partial Point Cloud Registration","date":"2022-08-04","arxiv_id":"2208.02712","n_code_links":1,"syntology":null},{"paper":"/paper/automatic-acne-object-detection-and-acne","slug":"automatic-acne-object-detection-and-acne","title":"Automatic Acne Object Detection and Acne Severity Grading Using Smartphone Images and Artificial Intelligence","date":"2022-08-03","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"combined-cnn-transformer-encoder-for-enhanced","title":"Combined CNN Transformer Encoder for Enhanced Fine-grained Human Action Recognition","date":"2022-08-03","arxiv_id":"2208.01897","n_code_links":0,"syntology":null},{"paper":null,"slug":"efficient-fine-tuning-of-compressed-language","title":"Efficient Fine-Tuning of Compressed Language Models with Learners","date":"2022-08-03","arxiv_id":"2208.02070","n_code_links":0,"syntology":null},{"paper":"/paper/image-based-detection-of-surface-defects-in","slug":"image-based-detection-of-surface-defects-in","title":"Image-based Detection of Surface Defects in Concrete during Construction","date":"2022-08-03","arxiv_id":"2208.02313","n_code_links":1,"syntology":null},{"paper":"/paper/multi-feature-vision-transformer-via-self","slug":"multi-feature-vision-transformer-via-self","title":"Multi-Feature Vision Transformer via Self-Supervised Representation Learning for Improvement of COVID-19 Diagnosis","date":"2022-08-03","arxiv_id":"2208.01843","n_code_links":1,"syntology":null},{"paper":"/paper/ssformer-a-lightweight-transformer-for","slug":"ssformer-a-lightweight-transformer-for","title":"SSformer: A Lightweight Transformer for Semantic Segmentation","date":"2022-08-03","arxiv_id":"2208.02034","n_code_links":1,"syntology":null},{"paper":"/paper/yolo-facev2-a-scale-and-occlusion-aware-face","slug":"yolo-facev2-a-scale-and-occlusion-aware-face","title":"YOLO-FaceV2: A Scale and Occlusion Aware Face Detector","date":"2022-08-03","arxiv_id":"2208.02019","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-comparative-study-on-covid-19-fake-news","title":"A Comparative Study on COVID-19 Fake News Detection Using Different Transformer Based Models","date":"2022-08-02","arxiv_id":"2208.01355","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-novel-transformer-network-with-shifted","title":"A Novel Transformer Network with Shifted Window Cross-Attention for Spatiotemporal Weather Forecasting","date":"2022-08-02","arxiv_id":"2208.01252","n_code_links":0,"syntology":null},{"paper":null,"slug":"active-entailment-encoding-for-explanation","title":"Active entailment encoding for explanation tree construction using parsimonious generation of hard negatives","date":"2022-08-02","arxiv_id":"2208.01376","n_code_links":0,"syntology":null},{"paper":null,"slug":"automatic-classification-of-bug-reports-based","title":"Automatic Classification of Bug Reports Based on Multiple Text Information and Reports' Intention","date":"2022-08-02","arxiv_id":"2208.01274","n_code_links":0,"syntology":null},{"paper":null,"slug":"exploring-gender-bias-in-retrieval-models","title":"Debiasing Gender Bias in Information Retrieval Models","date":"2022-08-02","arxiv_id":"2208.01755","n_code_links":0,"syntology":null},{"paper":"/paper/making-the-best-of-both-worlds-a-domain","slug":"making-the-best-of-both-worlds-a-domain","title":"Making the Best of Both Worlds: A Domain-Oriented Transformer for Unsupervised Domain Adaptation","date":"2022-08-02","arxiv_id":"2208.01195","n_code_links":1,"syntology":null},{"paper":null,"slug":"multi-module-g2p-converter-for-persian","title":"Multi-Module G2P Converter for Persian Focusing on Relations between Words","date":"2022-08-02","arxiv_id":"2208.01371","n_code_links":0,"syntology":null},{"paper":null,"slug":"two-stream-transformer-architecture-for-long","title":"Two-Stream Transformer Architecture for Long Video Understanding","date":"2022-08-02","arxiv_id":"2208.01753","n_code_links":0,"syntology":null},{"paper":"/paper/a-knee-cannot-have-lung-disease-out-of","slug":"a-knee-cannot-have-lung-disease-out-of","title":"A knee cannot have lung disease: out-of-distribution detection with in-distribution voting using the medical example of chest X-ray classification","date":"2022-08-01","arxiv_id":"2208.01077","n_code_links":1,"syntology":null},{"paper":"/paper/batman-bilateral-attention-transformer-in","slug":"batman-bilateral-attention-transformer-in","title":"BATMAN: Bilateral Attention Transformer in Motion-Appearance Neighboring Space for Video Object Segmentation","date":"2022-08-01","arxiv_id":"2208.01159","n_code_links":0,"syntology":null},{"paper":null,"slug":"field-aware-variational-autoencoders-for","title":"Field-aware Variational Autoencoders for Billion-scale User Representation Learning","date":"2022-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/gimlps-gate-with-inhibition-mechanism-in-mlps","slug":"gimlps-gate-with-inhibition-mechanism-in-mlps","title":"giMLPs: Gate with Inhibition Mechanism in MLPs","date":"2022-08-01","arxiv_id":"2208.00929","n_code_links":1,"syntology":null},{"paper":"/paper/local-perception-aware-transformer-for-aerial","slug":"local-perception-aware-transformer-for-aerial","title":"Local Perception-Aware Transformer for Aerial Tracking","date":"2022-08-01","arxiv_id":"2208.00662","n_code_links":1,"syntology":null},{"paper":"/paper/physics-inform-attention-temporal","slug":"physics-inform-attention-temporal","title":"Physics-inform attention temporal convolutional network for EEG-based motor imagery classification","date":"2022-08-01","arxiv_id":null,"n_code_links":2,"syntology":null},{"paper":null,"slug":"pose-uncertainty-aware-movement-synchrony","title":"Pose Uncertainty Aware Movement Synchrony Estimation via Spatial-Temporal Graph Transformer","date":"2022-08-01","arxiv_id":"2208.01161","n_code_links":0,"syntology":null},{"paper":"/paper/siamixformer-a-siamese-transformer-network","slug":"siamixformer-a-siamese-transformer-network","title":"SiamixFormer: a fully-transformer Siamese network with temporal Fusion for accurate building detection and change detection in bi-temporal remote sensing images","date":"2022-08-01","arxiv_id":"2208.00657","n_code_links":0,"syntology":null},{"paper":null,"slug":"studying-writer-suggestion-interaction-a","title":"Interacting with next-phrase suggestions: How suggestion systems aid and influence the cognitive processes of writing","date":"2022-08-01","arxiv_id":"2208.00636","n_code_links":0,"syntology":null},{"paper":"/paper/transdeeplab-convolution-free-transformer","slug":"transdeeplab-convolution-free-transformer","title":"TransDeepLab: Convolution-Free Transformer-based DeepLab v3+ for Medical Image Segmentation","date":"2022-08-01","arxiv_id":"2208.00713","n_code_links":1,"syntology":null},{"paper":"/paper/what-can-transformers-learn-in-context-a-case","slug":"what-can-transformers-learn-in-context-a-case","title":"What Can Transformers Learn In-Context? A Case Study of Simple Function Classes","date":"2022-08-01","arxiv_id":"2208.01066","n_code_links":2,"syntology":null},{"paper":"/paper/aggretriever-a-simple-approach-to-aggregate","slug":"aggretriever-a-simple-approach-to-aggregate","title":"Aggretriever: A Simple Approach to Aggregate Textual Representations for Robust Dense Passage Retrieval","date":"2022-07-31","arxiv_id":"2208.00511","n_code_links":1,"syntology":null},{"paper":null,"slug":"cloudattention-efficient-multi-scale","title":"CloudAttention: Efficient Multi-Scale Attention Scheme For 3D Point Cloud Learning","date":"2022-07-31","arxiv_id":"2208.00524","n_code_links":0,"syntology":null},{"paper":null,"slug":"neural-knowledge-bank-for-pretrained","title":"Neural Knowledge Bank for Pretrained Transformers","date":"2022-07-31","arxiv_id":"2208.00399","n_code_links":0,"syntology":null},{"paper":null,"slug":"one-for-all-one-stage-referring-expression","title":"One for All: One-stage Referring Expression Comprehension with Dynamic Reasoning","date":"2022-07-31","arxiv_id":"2208.00361","n_code_links":0,"syntology":null},{"paper":"/paper/strajnet-occupancy-flow-prediction-via-multi","slug":"strajnet-occupancy-flow-prediction-via-multi","title":"STrajNet: Multi-modal Hierarchical Transformer for Occupancy Flow Field Prediction in Autonomous Driving","date":"2022-07-31","arxiv_id":"2208.00394","n_code_links":1,"syntology":null},{"paper":"/paper/toward-understanding-wordart-corner-guided","slug":"toward-understanding-wordart-corner-guided","title":"Toward Understanding WordArt: Corner-Guided Transformer for Scene Text Recognition","date":"2022-07-31","arxiv_id":"2208.00438","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":["xdxie/wordart"],"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":"a-survey-on-masked-autoencoder-for-self","title":"A Survey on Masked Autoencoder for Self-supervised Learning in Vision and Beyond","date":"2022-07-30","arxiv_id":"2208.00173","n_code_links":0,"syntology":null}],"record_sha256":"8b6f8cd37f7c945045e07937646f00f4210e52e678a33a4b83b463c15842da05","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}