{"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/1x1-convolution/papers/15","list_of":"/method/1x1-convolution","method":"1x1 Convolution","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":15,"pages_in_order":57,"rows_per_page":100,"rows":[1401,1500],"of":5640,"counts":{"archive_papers_tagged":5640,"with_a_code_link":2516,"where_syntology_ran_a_sample":651,"not_listed_spam_title":0,"listed":5640,"listed_where_code_ran":651,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":545,"every_run_a_failure_of_syntologys_instrument":106,"listed_with_a_run_with_no_instrument_failure":545,"listed_every_run_a_failure_of_syntologys_instrument":106,"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/1x1-convolution","prev":"/method/1x1-convolution/papers/14","next":"/method/1x1-convolution/papers/16","papers":[{"paper":"/paper/edgeyolo-an-edge-real-time-object-detector","slug":"edgeyolo-an-edge-real-time-object-detector","title":"EdgeYOLO: An Edge-Real-Time Object Detector","date":"2023-02-15","arxiv_id":"2302.07483","n_code_links":1,"syntology":null},{"paper":"/paper/efficient-teacher-semi-supervised-object","slug":"efficient-teacher-semi-supervised-object","title":"Efficient Teacher: Semi-Supervised Object Detection for YOLOv5","date":"2023-02-15","arxiv_id":"2302.07577","n_code_links":4,"syntology":{"ran":3,"of":3,"n_ran_checked":3,"n_instrument":0,"unverified":0,"pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 2 honoured, 1 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["AlibabaResearch/efficientteacher"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/tizero-mastering-multi-agent-football-with","slug":"tizero-mastering-multi-agent-football-with","title":"TiZero: Mastering Multi-Agent Football with Curriculum Learning and Self-Play","date":"2023-02-15","arxiv_id":"2302.07515","n_code_links":1,"syntology":null},{"paper":"/paper/underwater-target-detection-based-on-improved","slug":"underwater-target-detection-based-on-improved","title":"Underwater target detection based on improved YOLOv7","date":"2023-02-14","arxiv_id":"2302.06939","n_code_links":1,"syntology":null},{"paper":null,"slug":"an-application-of-deep-learning-for-sweet","title":"An Application of Deep Learning for Sweet Cherry Phenotyping using YOLO Object Detection","date":"2023-02-13","arxiv_id":"2302.06698","n_code_links":0,"syntology":null},{"paper":"/paper/automatic-noise-filtering-with-dynamic-sparse","slug":"automatic-noise-filtering-with-dynamic-sparse","title":"Automatic Noise Filtering with Dynamic Sparse Training in Deep Reinforcement Learning","date":"2023-02-13","arxiv_id":"2302.06548","n_code_links":1,"syntology":{"ran":4,"of":4,"n_ran_checked":4,"n_instrument":0,"unverified":0,"pointer_only":1,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 1 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["bramgrooten/automatic-noise-filtering"],"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/cfnet-cascade-fusion-network-for-dense","slug":"cfnet-cascade-fusion-network-for-dense","title":"CEDNet: A Cascade Encoder-Decoder Network for Dense Prediction","date":"2023-02-13","arxiv_id":"2302.06052","n_code_links":2,"syntology":{"ran":10,"of":12,"n_ran_checked":8,"n_instrument":2,"unverified":2,"pointer_only":12,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","official":{"repos":["zhanggang001/cednet","zhanggang001/cfnet"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":"/paper/cholectriplet2022-show-me-a-tool-and-tell-me","slug":"cholectriplet2022-show-me-a-tool-and-tell-me","title":"CholecTriplet2022: Show me a tool and tell me the triplet -- an endoscopic vision challenge for surgical action triplet detection","date":"2023-02-13","arxiv_id":"2302.06294","n_code_links":2,"syntology":null},{"paper":null,"slug":"detection-and-segmentation-of-pancreas-using","title":"Detection and Segmentation of Pancreas using Morphological Snakes and Deep Convolutional Neural Networks","date":"2023-02-13","arxiv_id":"2302.06356","n_code_links":0,"syntology":null},{"paper":"/paper/implications-of-the-convergence-of-language","slug":"implications-of-the-convergence-of-language","title":"Do Vision and Language Models Share Concepts? A Vector Space Alignment Study","date":"2023-02-13","arxiv_id":"2302.06555","n_code_links":1,"syntology":{"ran":2,"of":2,"n_ran_checked":2,"n_instrument":0,"unverified":0,"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) · 0 unverified","official":{"repos":["jiaangli/vlca"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"inferring-player-location-in-sports-matches","title":"Inferring Player Location in Sports Matches: Multi-Agent Spatial Imputation from Limited Observations","date":"2023-02-13","arxiv_id":"2302.06569","n_code_links":0,"syntology":null},{"paper":"/paper/order-matters-agent-by-agent-policy","slug":"order-matters-agent-by-agent-policy","title":"Order Matters: Agent-by-agent Policy Optimization","date":"2023-02-13","arxiv_id":"2302.06205","n_code_links":1,"syntology":{"ran":4,"of":4,"n_ran_checked":3,"n_instrument":1,"unverified":0,"pointer_only":1,"phrase":"4 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; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["xihuai18/A2PO-ICLR2023"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/threatening-patch-attacks-on-object-detection","slug":"threatening-patch-attacks-on-object-detection","title":"Threatening Patch Attacks on Object Detection in Optical Remote Sensing Images","date":"2023-02-13","arxiv_id":"2302.06060","n_code_links":1,"syntology":null},{"paper":"/paper/self-supervised-pseudo-colorizing-of-masked","slug":"self-supervised-pseudo-colorizing-of-masked","title":"Self-supervised pseudo-colorizing of masked cells","date":"2023-02-12","arxiv_id":"2302.05968","n_code_links":2,"syntology":null},{"paper":"/paper/dual-relation-knowledge-distillation-for","slug":"dual-relation-knowledge-distillation-for","title":"Dual Relation Knowledge Distillation for Object Detection","date":"2023-02-11","arxiv_id":"2302.05637","n_code_links":1,"syntology":null},{"paper":null,"slug":"gcnet-probing-self-similarity-learning-for","title":"GCNet: Probing Self-Similarity Learning for Generalized Counting Network","date":"2023-02-10","arxiv_id":"2302.05132","n_code_links":0,"syntology":null},{"paper":"/paper/help-the-blind-see-assistance-for-the","slug":"help-the-blind-see-assistance-for-the","title":"Help the Blind See: Assistance for the Visually Impaired through Augmented Acoustic Simulation","date":"2023-02-09","arxiv_id":"2303.13536","n_code_links":1,"syntology":null},{"paper":"/paper/to-perceive-or-not-to-perceive-lightweight","slug":"to-perceive-or-not-to-perceive-lightweight","title":"To Perceive or Not to Perceive: Lightweight Stacked Hourglass Network","date":"2023-02-09","arxiv_id":"2302.04815","n_code_links":1,"syntology":null},{"paper":null,"slug":"an-efficient-instance-segmentation-approach","title":"An Efficient Instance Segmentation Approach for Extracting Fission Gas Bubbles on U-10Zr Annular Fuel","date":"2023-02-08","arxiv_id":"2302.12833","n_code_links":0,"syntology":null},{"paper":null,"slug":"short-term-memory-convolutions","title":"Short-Term Memory Convolutions","date":"2023-02-08","arxiv_id":"2302.04331","n_code_links":0,"syntology":null},{"paper":null,"slug":"swincross-cross-modal-swin-transformer-for","title":"SwinCross: Cross-modal Swin Transformer for Head-and-Neck Tumor Segmentation in PET/CT Images","date":"2023-02-08","arxiv_id":"2302.03861","n_code_links":0,"syntology":null},{"paper":null,"slug":"an-end-to-end-two-phase-deep-learning-based","title":"An End-to-End Two-Phase Deep Learning-Based workflow to Segment Man-made Objects Around Reservoirs","date":"2023-02-07","arxiv_id":"2302.03282","n_code_links":0,"syntology":null},{"paper":"/paper/boosting-zero-shot-classification-with","slug":"boosting-zero-shot-classification-with","title":"Diversity is Definitely Needed: Improving Model-Agnostic Zero-shot Classification via Stable Diffusion","date":"2023-02-07","arxiv_id":"2302.03298","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, 1 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["jordan-hs/diversity_is_definitely_needed"],"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":"impact-of-velocity-and-impact-angle-on","title":"Impact of velocity and impact angle on football shot accuracy during fundamental trainings","date":"2023-02-07","arxiv_id":"2302.03426","n_code_links":0,"syntology":null},{"paper":null,"slug":"lut-nn-towards-unified-neural-network","title":"LUT-NN: Empower Efficient Neural Network Inference with Centroid Learning and Table Lookup","date":"2023-02-07","arxiv_id":"2302.03213","n_code_links":0,"syntology":null},{"paper":null,"slug":"industrial-computed-tomography-based","title":"Industrial computed tomography based intelligent non-destructive testing method for power capacitor","date":"2023-02-06","arxiv_id":"2302.03601","n_code_links":0,"syntology":null},{"paper":null,"slug":"hyphen-a-hybrid-packing-method-and","title":"HyPHEN: A Hybrid Packing Method and Optimizations for Homomorphic Encryption-Based Neural Networks","date":"2023-02-05","arxiv_id":"2302.02407","n_code_links":0,"syntology":null},{"paper":"/paper/kdeformer-accelerating-transformers-via","slug":"kdeformer-accelerating-transformers-via","title":"KDEformer: Accelerating Transformers via Kernel Density Estimation","date":"2023-02-05","arxiv_id":"2302.02451","n_code_links":1,"syntology":{"ran":8,"of":12,"n_ran_checked":1,"n_instrument":7,"unverified":4,"pointer_only":0,"phrase":"8 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; 7 where Syntology's instrument failed) · 4 unverified","official":{"repos":["majid-daliri/kdeformer"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":4,"ran_from_kinds":["official"]}}},{"paper":"/paper/perfect-is-the-enemy-of-test-oracle","slug":"perfect-is-the-enemy-of-test-oracle","title":"Perfect is the enemy of test oracle","date":"2023-02-03","arxiv_id":"2302.01488","n_code_links":1,"syntology":null},{"paper":null,"slug":"on-suppressing-range-of-adaptive-stepsizes-of","title":"On Suppressing Range of Adaptive Stepsizes of Adam to Improve Generalisation Performance","date":"2023-02-02","arxiv_id":"2302.01029","n_code_links":0,"syntology":null},{"paper":"/paper/resilient-binary-neural-network","slug":"resilient-binary-neural-network","title":"Resilient Binary Neural Network","date":"2023-02-02","arxiv_id":"2302.00956","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 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":["stevetsui/rebnn"],"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":"/paper/adaptive-search-and-training-for-robust-and","slug":"adaptive-search-and-training-for-robust-and","title":"Adaptive Search-and-Training for Robust and Efficient Network Pruning","date":"2023-02-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/cross-modal-information-fusion-for-voice","slug":"cross-modal-information-fusion-for-voice","title":"Cross-modal information fusion for voice spoofing detection","date":"2023-02-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"qlab-quadratic-loss-approximation-based","title":"QLABGrad: a Hyperparameter-Free and Convergence-Guaranteed Scheme for Deep Learning","date":"2023-02-01","arxiv_id":"2302.00252","n_code_links":0,"syntology":null},{"paper":null,"slug":"amd-adaptive-masked-distillation-for-object","title":"AMD: Adaptive Masked Distillation for Object Detection","date":"2023-01-31","arxiv_id":"2301.13538","n_code_links":0,"syntology":null},{"paper":null,"slug":"design-and-implementation-of-a-soccer-ball","title":"Design and Implementation of A Soccer Ball Detection System with Multiple Cameras","date":"2023-01-31","arxiv_id":"2302.00123","n_code_links":0,"syntology":null},{"paper":"/paper/nasiam-efficient-representation-learning","slug":"nasiam-efficient-representation-learning","title":"NASiam: Efficient Representation Learning using Neural Architecture Search for Siamese Networks","date":"2023-01-31","arxiv_id":"2302.00059","n_code_links":1,"syntology":null},{"paper":null,"slug":"patch-gradient-descent-training-neural","title":"Patch Gradient Descent: Training Neural Networks on Very Large Images","date":"2023-01-31","arxiv_id":"2301.13817","n_code_links":0,"syntology":null},{"paper":"/paper/benchmarking-optimality-of-time-series","slug":"benchmarking-optimality-of-time-series","title":"Benchmarking optimality of time series classification methods in distinguishing diffusions","date":"2023-01-30","arxiv_id":"2301.13112","n_code_links":1,"syntology":null},{"paper":"/paper/depgraph-towards-any-structural-pruning","slug":"depgraph-towards-any-structural-pruning","title":"DepGraph: Towards Any Structural Pruning","date":"2023-01-30","arxiv_id":"2301.12900","n_code_links":1,"syntology":{"ran":7,"of":7,"n_ran_checked":7,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"7 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; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["VainF/Torch-Pruning"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/towards-verifying-the-geometric-robustness-of","slug":"towards-verifying-the-geometric-robustness-of","title":"Towards Verifying the Geometric Robustness of Large-scale Neural Networks","date":"2023-01-29","arxiv_id":"2301.12456","n_code_links":1,"syntology":{"ran":2,"of":2,"n_ran_checked":2,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"2 ran (of which 2 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) · 0 unverified; every one of the 2 samples that ran constructed an object rather than computing a result","official":{"repos":["trustai/georobust"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"deciphering-the-projection-head","title":"Deciphering the Projection Head: Representation Evaluation Self-supervised Learning","date":"2023-01-28","arxiv_id":"2301.12189","n_code_links":0,"syntology":null},{"paper":"/paper/efficient-latency-aware-cnn-depth-compression","slug":"efficient-latency-aware-cnn-depth-compression","title":"Efficient Latency-Aware CNN Depth Compression via Two-Stage Dynamic Programming","date":"2023-01-28","arxiv_id":"2301.12187","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 1 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 1 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","official":{"repos":["snu-mllab/efficient-cnn-depth-compression"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":1,"n_ran_no_instrument_failure":2,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"exploring-external-knowledge-for-accurate","title":"Exploring External Knowledge for Accurate modeling of Visual and Language Problems","date":"2023-01-27","arxiv_id":"2302.08901","n_code_links":0,"syntology":null},{"paper":null,"slug":"potential-penetrative-pass-p3","title":"Potential Penetrative Pass (P3)","date":"2023-01-26","arxiv_id":"2302.10760","n_code_links":0,"syntology":null},{"paper":"/paper/rewarded-meta-pruning-meta-learning-with","slug":"rewarded-meta-pruning-meta-learning-with","title":"Rewarded meta-pruning: Meta Learning with Rewards for Channel Pruning","date":"2023-01-26","arxiv_id":"2301.11063","n_code_links":1,"syntology":null},{"paper":"/paper/trainable-activations-for-image","slug":"trainable-activations-for-image","title":"Trainable Activations for Image Classification","date":"2023-01-26","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"event-detection-in-football-using-graph","title":"Event Detection in Football using Graph Convolutional Networks","date":"2023-01-24","arxiv_id":"2301.10052","n_code_links":0,"syntology":null},{"paper":"/paper/model-soups-to-increase-inference-without","slug":"model-soups-to-increase-inference-without","title":"Model soups to increase inference without increasing compute time","date":"2023-01-24","arxiv_id":"2301.10092","n_code_links":1,"syntology":null},{"paper":null,"slug":"progressive-meta-pooling-learning-for","title":"Progressive Meta-Pooling Learning for Lightweight Image Classification Model","date":"2023-01-24","arxiv_id":"2301.10038","n_code_links":0,"syntology":null},{"paper":"/paper/read-the-signs-towards-invariance-to-gradient","slug":"read-the-signs-towards-invariance-to-gradient","title":"Read the Signs: Towards Invariance to Gradient Descent's Hyperparameter Initialization","date":"2023-01-24","arxiv_id":"2301.10133","n_code_links":1,"syntology":null},{"paper":"/paper/classification-of-luminal-subtypes-in-full","slug":"classification-of-luminal-subtypes-in-full","title":"Classification of Luminal Subtypes in Full Mammogram Images Using Transfer Learning","date":"2023-01-23","arxiv_id":"2301.09282","n_code_links":0,"syntology":null},{"paper":null,"slug":"computer-vision-for-a-camel-vehicle-collision","title":"Computer Vision for a Camel-Vehicle Collision Mitigation System","date":"2023-01-23","arxiv_id":"2301.09339","n_code_links":0,"syntology":null},{"paper":null,"slug":"improving-presentation-attack-detection-for","title":"Improving Presentation Attack Detection for ID Cards on Remote Verification Systems","date":"2023-01-23","arxiv_id":"2301.09542","n_code_links":0,"syntology":null},{"paper":"/paper/local-window-attention-transformer-for","slug":"local-window-attention-transformer-for","title":"Local Window Attention Transformer for Polarimetric SAR Image Classification","date":"2023-01-23","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"a-comparative-analysis-of-cnn-based","title":"A Comparative Analysis of CNN-Based Pretrained Models for the Detection and Prediction of Monkeypox","date":"2023-01-20","arxiv_id":"2302.10277","n_code_links":0,"syntology":null},{"paper":null,"slug":"pneumonia-detection-in-chest-x-ray-images","title":"Pneumonia Detection in Chest X-Ray Images : Handling Class Imbalance","date":"2023-01-20","arxiv_id":"2301.08479","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-machine-learning-approach-for-player-and","title":"A Machine Learning Approach for Player and Position Adjusted Expected Goals in Football (Soccer)","date":"2023-01-19","arxiv_id":"2301.13052","n_code_links":0,"syntology":null},{"paper":"/paper/m3e-yolo-a-new-lightweight-network-for","slug":"m3e-yolo-a-new-lightweight-network-for","title":"M3E-Yolo: A New Lightweight Network for Traffic Sign Recognition","date":"2023-01-19","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"sentiment-analysis-for-measuring-hope-and","title":"Sentiment Analysis for Measuring Hope and Fear from Reddit Posts During the 2022 Russo-Ukrainian Conflict","date":"2023-01-19","arxiv_id":"2301.08347","n_code_links":0,"syntology":null},{"paper":null,"slug":"automated-deep-reinforcement-learning-for","title":"Automated deep reinforcement learning for real-time scheduling strategy of multi-energy system integrated with post-carbon and direct-air carbon captured system","date":"2023-01-18","arxiv_id":"2301.07768","n_code_links":0,"syntology":null},{"paper":"/paper/impact-of-the-euro-2020-championship-on-the","slug":"impact-of-the-euro-2020-championship-on-the","title":"Impact of the Euro 2020 championship on the spread of COVID-19","date":"2023-01-18","arxiv_id":"2301.07659","n_code_links":2,"syntology":null},{"paper":null,"slug":"a-2-uav-application-aware-content-and-network","title":"A$^2$-UAV: Application-Aware Content and Network Optimization of Edge-Assisted UAV Systems","date":"2023-01-16","arxiv_id":"2301.06363","n_code_links":0,"syntology":null},{"paper":"/paper/a-semi-trailer-truck-right-hook-turn-blind","slug":"a-semi-trailer-truck-right-hook-turn-blind","title":"Monocular Cyclist Detection with Convolutional Neural Networks","date":"2023-01-16","arxiv_id":"2303.11223","n_code_links":0,"syntology":null},{"paper":"/paper/multimodal-side-tuning-for-document","slug":"multimodal-side-tuning-for-document","title":"Multimodal Side-Tuning for Document Classification","date":"2023-01-16","arxiv_id":"2301.07502","n_code_links":1,"syntology":null},{"paper":null,"slug":"lb-simtsc-an-efficient-similarity-aware-graph","title":"LB-SimTSC: An Efficient Similarity-Aware Graph Neural Network for Semi-Supervised Time Series Classification","date":"2023-01-12","arxiv_id":"2301.04838","n_code_links":0,"syntology":null},{"paper":"/paper/semppl-predicting-pseudo-labels-for-better","slug":"semppl-predicting-pseudo-labels-for-better","title":"SemPPL: Predicting pseudo-labels for better contrastive representations","date":"2023-01-12","arxiv_id":"2301.05158","n_code_links":2,"syntology":{"ran":12,"of":16,"n_ran_checked":12,"n_instrument":0,"unverified":4,"pointer_only":0,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 12 with no instrument failure: 0 honoured, 0 violated, 12 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","official":{"repos":["deepmind/semppl","google-deepmind/semppl"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":12,"n_unverified":4,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"deep-residual-axial-networks","title":"Deep Residual Axial Networks","date":"2023-01-11","arxiv_id":"2301.04631","n_code_links":0,"syntology":null},{"paper":null,"slug":"enhancing-resnet-image-classification","title":"Enhancing ResNet Image Classification Performance by using Parameterized Hypercomplex Multiplication","date":"2023-01-11","arxiv_id":"2301.04623","n_code_links":0,"syntology":null},{"paper":null,"slug":"does-image-resolution-impact-chest-x-ray","title":"Does image resolution impact chest X-ray based fine-grained Tuberculosis-consistent lesion segmentation?","date":"2023-01-10","arxiv_id":"2301.04032","n_code_links":0,"syntology":null},{"paper":null,"slug":"deep-learning-for-classification-of-chest-x","title":"Deep Learning For Classification Of Chest X-Ray Images (Covid 19)","date":"2023-01-06","arxiv_id":"2301.02468","n_code_links":0,"syntology":null},{"paper":null,"slug":"deep-learning-models-in-medical-image","title":"Deep-learning models in medical image analysis: Detection of esophagitis from the Kvasir Dataset","date":"2023-01-06","arxiv_id":"2301.02390","n_code_links":0,"syntology":null},{"paper":null,"slug":"designing-an-improved-deep-learning-based","title":"Designing an Improved Deep Learning-based Model for COVID-19 Recognition in Chest X-ray Images: A Knowledge Distillation Approach","date":"2023-01-06","arxiv_id":"2301.02735","n_code_links":0,"syntology":null},{"paper":null,"slug":"saids-a-novel-approach-for-sentiment-analysis","title":"SAIDS: A Novel Approach for Sentiment Analysis Informed of Dialect and Sarcasm","date":"2023-01-06","arxiv_id":"2301.02521","n_code_links":0,"syntology":null},{"paper":"/paper/extreme-q-learning-maxent-rl-without-entropy","slug":"extreme-q-learning-maxent-rl-without-entropy","title":"Extreme Q-Learning: MaxEnt RL without Entropy","date":"2023-01-05","arxiv_id":"2301.02328","n_code_links":4,"syntology":{"ran":8,"of":13,"n_ran_checked":5,"n_instrument":3,"unverified":5,"pointer_only":7,"phrase":"8 ran (of which 5 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 3 where Syntology's instrument failed) · 5 unverified","official":null}},{"paper":null,"slug":"groma-a-tool-for-measuring-deep-neural","title":"gRoMA: a Tool for Measuring the Global Robustness of Deep Neural Networks","date":"2023-01-05","arxiv_id":"2301.02288","n_code_links":0,"syntology":null},{"paper":null,"slug":"lostnet-a-smart-way-for-lost-and-find","title":"LostNet: A smart way for lost and find","date":"2023-01-05","arxiv_id":"2301.02277","n_code_links":0,"syntology":null},{"paper":null,"slug":"training-trajectories-mini-batch-losses-and","title":"Training trajectories, mini-batch losses and the curious role of the learning rate","date":"2023-01-05","arxiv_id":"2301.02312","n_code_links":0,"syntology":null},{"paper":null,"slug":"conscious-brain-mind-controlled-cybonthitic","title":"A Novel Power-optimized CMOS sEMG Device with Ultra Low-noise integrated with ConvNet (VGG16) for Biomedical Applications","date":"2023-01-04","arxiv_id":"2301.09570","n_code_links":0,"syntology":null},{"paper":null,"slug":"towards-edge-cloud-architectures-for-personal","title":"Towards Edge-Cloud Architectures for Personal Protective Equipment Detection","date":"2023-01-04","arxiv_id":"2301.01501","n_code_links":0,"syntology":null},{"paper":null,"slug":"improving-performance-in-neural-networks-by","title":"Increasing biases can be more efficient than increasing weights","date":"2023-01-03","arxiv_id":"2301.00924","n_code_links":0,"syntology":null},{"paper":"/paper/rethinking-mobile-block-for-efficient-neural","slug":"rethinking-mobile-block-for-efficient-neural","title":"Rethinking Mobile Block for Efficient Attention-based Models","date":"2023-01-03","arxiv_id":"2301.01146","n_code_links":1,"syntology":null},{"paper":null,"slug":"bit-shrinking-limiting-instantaneous","title":"Bit-Shrinking: Limiting Instantaneous Sharpness for Improving Post-Training Quantization","date":"2023-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"csda-learning-category-scale-joint-feature","title":"CSDA: Learning Category-Scale Joint Feature for Domain Adaptive Object Detection","date":"2023-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"exploring-intra-class-variation-factors-with","title":"Exploring Intra-Class Variation Factors With Learnable Cluster Prompts for Semi-Supervised Image Synthesis","date":"2023-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"masked-autoencoders-are-stronger-knowledge","title":"Masked Autoencoders Are Stronger Knowledge Distillers","date":"2023-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/poincare-resnet-1","slug":"poincare-resnet-1","title":"Poincare ResNet","date":"2023-01-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/robust-domain-adaptive-object-detection-with","slug":"robust-domain-adaptive-object-detection-with","title":"Robust Domain Adaptive Object Detection with Unified Multi-Granularity Alignment","date":"2023-01-01","arxiv_id":"2301.00371","n_code_links":1,"syntology":null},{"paper":null,"slug":"toplight-lightweight-neural-networks-with","title":"TOPLight: Lightweight Neural Networks With Task-Oriented Pretraining for Visible-Infrared Recognition","date":"2023-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/viewnet-a-novel-projection-based-backbone","slug":"viewnet-a-novel-projection-based-backbone","title":"ViewNet: A Novel Projection-Based Backbone With View Pooling for Few-Shot Point Cloud Classification","date":"2023-01-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"autonomous-driving-simulator-based-on","title":"Autonomous Driving Simulator based on Neurorobotics Platform","date":"2022-12-31","arxiv_id":"2301.00089","n_code_links":0,"syntology":null},{"paper":"/paper/chest-x-ray-images-classification-with-cnn","slug":"chest-x-ray-images-classification-with-cnn","title":"Chest X-Ray Images Classification with CNN","date":"2022-12-31","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"a-comparison-study-of-deep-cnn-architecture","title":"A Comparison Study of Deep CNN Architecture in Detecting of Pneumonia","date":"2022-12-30","arxiv_id":"2212.14744","n_code_links":0,"syntology":null},{"paper":"/paper/evaluating-generalizability-of-deep-learning","slug":"evaluating-generalizability-of-deep-learning","title":"Evaluating Generalizability of Deep Learning Models Using Indian-COVID-19 CT Dataset","date":"2022-12-28","arxiv_id":"2212.13929","n_code_links":1,"syntology":null},{"paper":null,"slug":"myi-net-fully-automatic-detection-and","title":"MyI-Net: Fully Automatic Detection and Quantification of Myocardial Infarction from Cardiovascular MRI Images","date":"2022-12-28","arxiv_id":"2212.13715","n_code_links":0,"syntology":null},{"paper":null,"slug":"towards-automating-codenames-spymasters-with","title":"Towards automating Codenames spymasters with deep reinforcement learning","date":"2022-12-28","arxiv_id":"2212.14104","n_code_links":0,"syntology":null},{"paper":null,"slug":"human-activity-recognition-from-wi-fi-csi","title":"Human Activity Recognition from Wi-Fi CSI Data Using Principal Component-Based Wavelet CNN","date":"2022-12-26","arxiv_id":"2212.13161","n_code_links":0,"syntology":null},{"paper":null,"slug":"omsn-and-faros-octa-microstructure","title":"OMSN and FAROS: OCTA Microstructure Segmentation Network and Fully Annotated Retinal OCTA Segmentation Dataset","date":"2022-12-26","arxiv_id":"2212.13059","n_code_links":0,"syntology":null},{"paper":null,"slug":"pruning-on-the-fly-a-recoverable-pruning","title":"Pruning On-the-Fly: A Recoverable Pruning Method without Fine-tuning","date":"2022-12-24","arxiv_id":"2212.12651","n_code_links":0,"syntology":null},{"paper":null,"slug":"unpaired-overwater-image-defogging-using","title":"Unpaired Overwater Image Defogging Using Prior Map Guided CycleGAN","date":"2022-12-23","arxiv_id":"2212.12116","n_code_links":0,"syntology":null}],"record_sha256":"44b4631243af84e226a2067737abd35ea292d081c261c1d8f697e9faa74ae24d","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}