{"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/relu/papers/32","list_of":"/method/relu","method":"ReLU","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":32,"pages_in_order":104,"rows_per_page":100,"rows":[3101,3200],"of":10350,"counts":{"archive_papers_tagged":10350,"with_a_code_link":4256,"where_syntology_ran_a_sample":1079,"not_listed_spam_title":0,"listed":10350,"listed_where_code_ran":1079,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":909,"every_run_a_failure_of_syntologys_instrument":170,"listed_with_a_run_with_no_instrument_failure":909,"listed_every_run_a_failure_of_syntologys_instrument":170,"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/relu","prev":"/method/relu/papers/31","next":"/method/relu/papers/33","papers":[{"paper":null,"slug":"feedback-assisted-adversarial-learning-to","title":"Feedback Assisted Adversarial Learning to Improve the Quality of Cone-beam CT Images","date":"2022-10-23","arxiv_id":"2210.12578","n_code_links":0,"syntology":null},{"paper":"/paper/hifi-wavegan-generative-adversarial-network","slug":"hifi-wavegan-generative-adversarial-network","title":"HiFi-WaveGAN: Generative Adversarial Network with Auxiliary Spectrogram-Phase Loss for High-Fidelity Singing Voice Generation","date":"2022-10-23","arxiv_id":"2210.12740","n_code_links":1,"syntology":null},{"paper":null,"slug":"vp-slam-a-monocular-real-time-visual-slam","title":"VP-SLAM: A Monocular Real-time Visual SLAM with Points, Lines and Vanishing Points","date":"2022-10-23","arxiv_id":"2210.12756","n_code_links":0,"syntology":null},{"paper":null,"slug":"diversity-promoting-ensemble-for-medical","title":"Diversity-Promoting Ensemble for Medical Image Segmentation","date":"2022-10-22","arxiv_id":"2210.12388","n_code_links":0,"syntology":null},{"paper":"/paper/s2wat-image-style-transfer-via-hierarchical","slug":"s2wat-image-style-transfer-via-hierarchical","title":"S2WAT: Image Style Transfer via Hierarchical Vision Transformer using Strips Window Attention","date":"2022-10-22","arxiv_id":"2210.12381","n_code_links":1,"syntology":null},{"paper":null,"slug":"adaptive-re-calibration-of-channel-wise","title":"Adaptive re-calibration of channel-wise features for Adversarial Audio Classification","date":"2022-10-21","arxiv_id":"2210.11722","n_code_links":0,"syntology":null},{"paper":null,"slug":"group-distributionally-robust-reinforcement","title":"Group Distributionally Robust Reinforcement Learning with Hierarchical Latent Variables","date":"2022-10-21","arxiv_id":"2210.12262","n_code_links":0,"syntology":null},{"paper":null,"slug":"planning-with-uncertainty-deep-exploration-in","title":"Epistemic Monte Carlo Tree Search","date":"2022-10-21","arxiv_id":"2210.13455","n_code_links":0,"syntology":null},{"paper":"/paper/task-based-assessment-for-neural-networks","slug":"task-based-assessment-for-neural-networks","title":"Task-Based Assessment for Neural Networks: Evaluating Undersampled MRI Reconstructions based on Human Observer Signal Detection","date":"2022-10-21","arxiv_id":"2210.12161","n_code_links":1,"syntology":null},{"paper":null,"slug":"improving-semi-supervised-end-to-end","title":"Improving Semi-supervised End-to-end Automatic Speech Recognition using CycleGAN and Inter-domain Losses","date":"2022-10-20","arxiv_id":"2210.11642","n_code_links":0,"syntology":null},{"paper":null,"slug":"overexposure-mask-fusion-generalizable","title":"Overexposure Mask Fusion: Generalizable Reverse ISP Multi-Step Refinement","date":"2022-10-20","arxiv_id":"2210.11511","n_code_links":0,"syntology":null},{"paper":null,"slug":"single-image-super-resolution-using-2","title":"Single Image Super-Resolution Using Lightweight Networks Based on Swin Transformer","date":"2022-10-20","arxiv_id":"2210.11019","n_code_links":0,"syntology":null},{"paper":null,"slug":"comparative-analysis-of-deep-learning-1","title":"Comparative analysis of deep learning approaches for AgNOR-stained cytology samples interpretation","date":"2022-10-19","arxiv_id":"2210.10641","n_code_links":0,"syntology":null},{"paper":"/paper/propagating-variational-model-uncertainty-for","slug":"propagating-variational-model-uncertainty-for","title":"Propagating Variational Model Uncertainty for Bioacoustic Call Label Smoothing","date":"2022-10-19","arxiv_id":"2210.10526","n_code_links":1,"syntology":null},{"paper":null,"slug":"segmentation-free-direct-iris-localization","title":"Segmentation-free Direct Iris Localization Networks","date":"2022-10-19","arxiv_id":"2210.10403","n_code_links":0,"syntology":null},{"paper":null,"slug":"visual-slam-what-are-the-current-trends-and","title":"Visual SLAM: What are the Current Trends and What to Expect?","date":"2022-10-19","arxiv_id":"2210.10491","n_code_links":0,"syntology":null},{"paper":"/paper/a-novel-statistical-methodology-for","slug":"a-novel-statistical-methodology-for","title":"A novel statistical methodology for quantifying the spatial arrangements of axons in peripheral nerves","date":"2022-10-18","arxiv_id":"2210.09554","n_code_links":1,"syntology":null},{"paper":"/paper/automatic-separation-of-laminar-turbulent","slug":"automatic-separation-of-laminar-turbulent","title":"Automatic separation of laminar-turbulent flows on aircraft wings and stabilisers via adaptive attention butterfly network","date":"2022-10-18","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"if-gan-a-novel-generator-architecture-with","title":"Improving GANs with a Feature Cycling Generator","date":"2022-10-18","arxiv_id":"2210.09638","n_code_links":0,"syntology":null},{"paper":null,"slug":"transfer-learning-with-weak-labels-from","title":"Weakly Supervised Learning with Automated Labels from Radiology Reports for Glioma Change Detection","date":"2022-10-18","arxiv_id":"2210.09698","n_code_links":0,"syntology":null},{"paper":"/paper/unsupervised-visualization-of-image-datasets","slug":"unsupervised-visualization-of-image-datasets","title":"Unsupervised visualization of image datasets using contrastive learning","date":"2022-10-18","arxiv_id":"2210.09879","n_code_links":1,"syntology":{"ran":30,"of":34,"n_ran_checked":18,"n_instrument":12,"unverified":4,"pointer_only":34,"phrase":"30 ran (of which 14 constructed an object rather than computing a result; 18 with no instrument failure: 0 honoured, 0 violated, 18 with no contract checked; 12 where Syntology's instrument failed) · 4 unverified","official":{"repos":["berenslab/t-simcne"],"state":"official (archive's flag): 30 ran","n_ran":30,"n_constructed":14,"n_ran_no_instrument_failure":18,"n_unverified":4,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"a-transfer-learning-based-approach-for-1","title":"A Transfer Learning Based Approach for Classification of COVID-19 and Pneumonia in CT Scan Imaging","date":"2022-10-17","arxiv_id":"2210.09403","n_code_links":0,"syntology":null},{"paper":null,"slug":"anisotropic-multi-scale-graph-convolutional","title":"Anisotropic Multi-Scale Graph Convolutional Network for Dense Shape Correspondence","date":"2022-10-17","arxiv_id":"2210.09466","n_code_links":0,"syntology":null},{"paper":null,"slug":"data-driven-short-term-daily-operational-sea","title":"Data-Driven Short-Term Daily Operational Sea Ice Regional Forecasting","date":"2022-10-17","arxiv_id":"2210.08877","n_code_links":0,"syntology":null},{"paper":null,"slug":"ptde-personalized-training-with-distillated","title":"PTDE: Personalized Training with Distilled Execution for Multi-Agent Reinforcement Learning","date":"2022-10-17","arxiv_id":"2210.08872","n_code_links":0,"syntology":null},{"paper":"/paper/self-supervised-learning-through-efference","slug":"self-supervised-learning-through-efference","title":"Self-Supervised Learning Through Efference Copies","date":"2022-10-17","arxiv_id":"2210.09224","n_code_links":1,"syntology":null},{"paper":"/paper/accelerating-transfer-learning-with-near-data","slug":"accelerating-transfer-learning-with-near-data","title":"Accelerating Transfer Learning with Near-Data Computation on Cloud Object Stores","date":"2022-10-16","arxiv_id":"2210.08650","n_code_links":1,"syntology":null},{"paper":null,"slug":"object-attentional-untargeted-adversarial","title":"Object-Attentional Untargeted Adversarial Attack","date":"2022-10-16","arxiv_id":"2210.08472","n_code_links":0,"syntology":null},{"paper":null,"slug":"robust-general-and-low-complexity-acoustic","title":"Robust, General, and Low Complexity Acoustic Scene Classification Systems and An Effective Visualization for Presenting a Sound Scene Context","date":"2022-10-16","arxiv_id":"2210.08610","n_code_links":0,"syntology":null},{"paper":"/paper/self-improving-slam-in-dynamic-environments","slug":"self-improving-slam-in-dynamic-environments","title":"Self-Improving SLAM in Dynamic Environments: Learning When to Mask","date":"2022-10-15","arxiv_id":"2210.08350","n_code_links":1,"syntology":null},{"paper":null,"slug":"an-efficient-fpga-accelerator-for-point-cloud","title":"An Efficient FPGA Accelerator for Point Cloud","date":"2022-10-14","arxiv_id":"2210.07803","n_code_links":0,"syntology":null},{"paper":null,"slug":"comparison-of-different-automatic-solutions","title":"Comparison of different automatic solutions for resection cavity segmentation in postoperative MRI volumes including longitudinal acquisitions","date":"2022-10-14","arxiv_id":"2210.07806","n_code_links":0,"syntology":null},{"paper":"/paper/exploring-vanilla-u-net-for-lesion","slug":"exploring-vanilla-u-net-for-lesion","title":"Exploring Vanilla U-Net for Lesion Segmentation from Whole-body FDG-PET/CT Scans","date":"2022-10-14","arxiv_id":"2210.07490","n_code_links":1,"syntology":null},{"paper":"/paper/improved-automated-lesion-segmentation-in","slug":"improved-automated-lesion-segmentation-in","title":"Improved automated lesion segmentation in whole-body FDG/PET-CT via Test-Time Augmentation","date":"2022-10-14","arxiv_id":"2210.07761","n_code_links":1,"syntology":null},{"paper":null,"slug":"neural-network-compression-by-joint-sparsity","title":"Neural Network Compression by Joint Sparsity Promotion and Redundancy Reduction","date":"2022-10-14","arxiv_id":"2210.07451","n_code_links":0,"syntology":null},{"paper":null,"slug":"synthetic-to-real-composite-semantic","title":"Synthetic-to-real Composite Semantic Segmentation in Additive Manufacturing","date":"2022-10-14","arxiv_id":"2210.07466","n_code_links":0,"syntology":null},{"paper":null,"slug":"wide-range-mri-artifact-removal-with","title":"Wide Range MRI Artifact Removal with Transformers","date":"2022-10-14","arxiv_id":"2210.07976","n_code_links":0,"syntology":null},{"paper":"/paper/demystifying-self-supervised-trojan-attacks","slug":"demystifying-self-supervised-trojan-attacks","title":"An Embarrassingly Simple Backdoor Attack on Self-supervised Learning","date":"2022-10-13","arxiv_id":"2210.07346","n_code_links":4,"syntology":{"ran":2,"of":2,"n_ran_checked":0,"n_instrument":2,"unverified":0,"pointer_only":2,"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":["meet-cjli/ctrl","CCCjiang/CTRL"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":null,"slug":"equi-tuning-group-equivariant-fine-tuning-of","title":"Equi-Tuning: Group Equivariant Fine-Tuning of Pretrained Models","date":"2022-10-13","arxiv_id":"2210.06475","n_code_links":0,"syntology":null},{"paper":"/paper/pdebench-an-extensive-benchmark-for","slug":"pdebench-an-extensive-benchmark-for","title":"PDEBENCH: An Extensive Benchmark for Scientific Machine Learning","date":"2022-10-13","arxiv_id":"2210.07182","n_code_links":5,"syntology":{"ran":4,"of":5,"n_ran_checked":4,"n_instrument":0,"unverified":1,"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) · 1 unverified","official":{"repos":["pdebench/pdebench"],"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/the-hidden-uniform-cluster-prior-in-self","slug":"the-hidden-uniform-cluster-prior-in-self","title":"The Hidden Uniform Cluster Prior in Self-Supervised Learning","date":"2022-10-13","arxiv_id":"2210.07277","n_code_links":1,"syntology":null},{"paper":"/paper/u-hrnet-delving-into-improving-semantic","slug":"u-hrnet-delving-into-improving-semantic","title":"U-HRNet: Delving into Improving Semantic Representation of High Resolution Network for Dense Prediction","date":"2022-10-13","arxiv_id":"2210.07140","n_code_links":4,"syntology":null},{"paper":"/paper/wasserstein-barycenter-based-model-fusion-and","slug":"wasserstein-barycenter-based-model-fusion-and","title":"Wasserstein Barycenter-based Model Fusion and Linear Mode Connectivity of Neural Networks","date":"2022-10-13","arxiv_id":"2210.06671","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-comparative-study-on-1-5t-3t-mri-conversion","title":"A Comparative Study on 1.5T-3T MRI Conversion through Deep Neural Network Models","date":"2022-10-12","arxiv_id":"2210.06362","n_code_links":0,"syntology":null},{"paper":"/paper/a-unified-model-for-reconstruction-and-r2","slug":"a-unified-model-for-reconstruction-and-r2","title":"A unified model for reconstruction and R2* mapping of accelerated 7T data using the quantitative recurrent inference machine","date":"2022-10-12","arxiv_id":null,"n_code_links":2,"syntology":null},{"paper":null,"slug":"betting-the-system-using-lineups-to-predict","title":"Betting the system: Using lineups to predict football scores","date":"2022-10-12","arxiv_id":"2210.06327","n_code_links":0,"syntology":null},{"paper":"/paper/efficient-offline-policy-optimization-with-a","slug":"efficient-offline-policy-optimization-with-a","title":"Efficient Offline Policy Optimization with a Learned Model","date":"2022-10-12","arxiv_id":"2210.05980","n_code_links":1,"syntology":null},{"paper":"/paper/flare7k-a-phenomenological-nighttime-flare","slug":"flare7k-a-phenomenological-nighttime-flare","title":"Flare7K: A Phenomenological Nighttime Flare Removal Dataset","date":"2022-10-12","arxiv_id":"2210.06570","n_code_links":1,"syntology":null},{"paper":null,"slug":"quantifying-u-net-uncertainty-in-multi","title":"Quantifying U-Net Uncertainty in Multi-Parametric MRI-based Glioma Segmentation by Spherical Image Projection","date":"2022-10-12","arxiv_id":"2210.06512","n_code_links":0,"syntology":null},{"paper":null,"slug":"improving-dense-contrastive-learning-with","title":"Improving Dense Contrastive Learning with Dense Negative Pairs","date":"2022-10-11","arxiv_id":"2210.05063","n_code_links":0,"syntology":null},{"paper":null,"slug":"ugformer-for-robust-left-atrium-and-scar","title":"UGformer for Robust Left Atrium and Scar Segmentation Across Scanners","date":"2022-10-11","arxiv_id":"2210.05151","n_code_links":0,"syntology":null},{"paper":"/paper/what-does-a-deep-neural-network-confidently","slug":"what-does-a-deep-neural-network-confidently","title":"What does a deep neural network confidently perceive? The effective dimension of high certainty class manifolds and their low confidence boundaries","date":"2022-10-11","arxiv_id":"2210.05546","n_code_links":1,"syntology":null},{"paper":"/paper/multiagent-reinforcement-learning-based-on","slug":"multiagent-reinforcement-learning-based-on","title":"Multiagent Reinforcement Learning Based on Fusion-Multiactor-Attention-Critic for Multiple-Unmanned-Aerial-Vehicle Navigation Control","date":"2022-10-10","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/rejecting-noise-in-baikal-gvd-data-with","slug":"rejecting-noise-in-baikal-gvd-data-with","title":"Rejecting noise in Baikal-GVD data with neural networks","date":"2022-10-10","arxiv_id":"2210.04653","n_code_links":1,"syntology":null},{"paper":"/paper/sampling-based-inference-for-large-linear","slug":"sampling-based-inference-for-large-linear","title":"Sampling-based inference for large linear models, with application to linearised Laplace","date":"2022-10-10","arxiv_id":"2210.04994","n_code_links":1,"syntology":{"ran":2,"of":4,"n_ran_checked":2,"n_instrument":0,"unverified":2,"pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","official":{"repos":["cambridge-mlg/sampled-laplace"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"scale-equivariant-u-net","title":"Scale Equivariant U-Net","date":"2022-10-10","arxiv_id":"2210.04508","n_code_links":0,"syntology":null},{"paper":null,"slug":"using-detection-tracking-and-prediction-in","title":"Using Detection, Tracking and Prediction in Visual SLAM to Achieve Real-time Semantic Mapping of Dynamic Scenarios","date":"2022-10-10","arxiv_id":"2210.04562","n_code_links":0,"syntology":null},{"paper":null,"slug":"using-whole-slide-image-representations-from","title":"Using Whole Slide Image Representations from Self-Supervised Contrastive Learning for Melanoma Concordance Regression","date":"2022-10-10","arxiv_id":"2210.04803","n_code_links":0,"syntology":null},{"paper":null,"slug":"adapting-pretrained-vision-language","title":"Adapting Pretrained Vision-Language Foundational Models to Medical Imaging Domains","date":"2022-10-09","arxiv_id":"2210.04133","n_code_links":0,"syntology":null},{"paper":"/paper/elign-expectation-alignment-as-a-multi-agent","slug":"elign-expectation-alignment-as-a-multi-agent","title":"ELIGN: Expectation Alignment as a Multi-Agent Intrinsic Reward","date":"2022-10-09","arxiv_id":"2210.04365","n_code_links":1,"syntology":null},{"paper":null,"slug":"sml-enhance-the-network-smoothness-with-skip","title":"SML:Enhance the Network Smoothness with Skip Meta Logit for CTR Prediction","date":"2022-10-09","arxiv_id":"2210.10725","n_code_links":0,"syntology":null},{"paper":null,"slug":"text-detection-and-recognition-based-on-a","title":"Text detection and recognition based on a lensless imaging system","date":"2022-10-09","arxiv_id":"2210.04244","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-deep-learning-network-with-differentiable","title":"A deep learning network with differentiable dynamic programming for retina OCT surface segmentation","date":"2022-10-08","arxiv_id":"2210.06335","n_code_links":0,"syntology":null},{"paper":"/paper/fbnet-feedback-network-for-point-cloud","slug":"fbnet-feedback-network-for-point-cloud","title":"FBNet: Feedback Network for Point Cloud Completion","date":"2022-10-08","arxiv_id":"2210.03974","n_code_links":1,"syntology":null},{"paper":null,"slug":"lw-isp-a-lightweight-model-with-isp-and-deep","title":"LW-ISP: A Lightweight Model with ISP and Deep Learning","date":"2022-10-08","arxiv_id":"2210.03904","n_code_links":0,"syntology":null},{"paper":"/paper/a-closer-look-at-hardware-friendly-weight","slug":"a-closer-look-at-hardware-friendly-weight","title":"A Closer Look at Hardware-Friendly Weight Quantization","date":"2022-10-07","arxiv_id":"2210.03671","n_code_links":1,"syntology":null},{"paper":null,"slug":"algorithmic-trading-using-continuous-action","title":"Algorithmic Trading Using Continuous Action Space Deep Reinforcement Learning","date":"2022-10-07","arxiv_id":"2210.03469","n_code_links":0,"syntology":null},{"paper":"/paper/images-as-weight-matrices-sequential-image","slug":"images-as-weight-matrices-sequential-image","title":"Images as Weight Matrices: Sequential Image Generation Through Synaptic Learning Rules","date":"2022-10-07","arxiv_id":"2210.06184","n_code_links":1,"syntology":{"ran":4,"of":4,"n_ran_checked":3,"n_instrument":1,"unverified":0,"pointer_only":4,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 2 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["idsia/fpainter"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official","unlocated"]}}},{"paper":"/paper/in-search-of-a-robust-facial-expressions","slug":"in-search-of-a-robust-facial-expressions","title":"In Search of a Robust Facial Expressions Recognition Model: A Large-Scale Visual Cross-Corpus Study","date":"2022-10-07","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"synthetic-voice-detection-and-audio-splicing","title":"Synthetic Voice Detection and Audio Splicing Detection using SE-Res2Net-Conformer Architecture","date":"2022-10-07","arxiv_id":"2210.03581","n_code_links":0,"syntology":null},{"paper":"/paper/temporal-feature-alignment-in-contrastive","slug":"temporal-feature-alignment-in-contrastive","title":"Temporal Feature Alignment in Contrastive Self-Supervised Learning for Human Activity Recognition","date":"2022-10-07","arxiv_id":"2210.03382","n_code_links":1,"syntology":null},{"paper":"/paper/a-resnet-is-all-you-need-modeling-a-strong","slug":"a-resnet-is-all-you-need-modeling-a-strong","title":"A ResNet is All You Need? Modeling A Strong Baseline for Detecting Referable Diabetic Retinopathy in Fundus Images","date":"2022-10-06","arxiv_id":"2210.03180","n_code_links":1,"syntology":null},{"paper":null,"slug":"brain-structure-can-mediate-or-moderate-the","title":"Brain structure can mediate or moderate the relationship of behavior to brain function and transcriptome. A preliminary study","date":"2022-10-06","arxiv_id":"2210.03195","n_code_links":0,"syntology":null},{"paper":null,"slug":"dual-stage-deeply-supervised-attention-based","title":"Dual-Stage Deeply Supervised Attention-based Convolutional Neural Networks for Mandibular Canal Segmentation in CBCT Scans","date":"2022-10-06","arxiv_id":"2210.03739","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-novel-entropy-maximizing-td3-based","title":"A Novel Entropy-Maximizing TD3-based Reinforcement Learning for Automatic PID Tuning","date":"2022-10-05","arxiv_id":"2210.02381","n_code_links":0,"syntology":null},{"paper":null,"slug":"advanced-deep-learning-architectures-for","title":"Advanced Deep Learning Architectures for Accurate Detection of Subsurface Tile Drainage Pipes from Remote Sensing Images","date":"2022-10-05","arxiv_id":"2210.02071","n_code_links":0,"syntology":null},{"paper":"/paper/bi-stride-multi-scale-graph-neural-network","slug":"bi-stride-multi-scale-graph-neural-network","title":"Efficient Learning of Mesh-Based Physical Simulation with BSMS-GNN","date":"2022-10-05","arxiv_id":"2210.02573","n_code_links":1,"syntology":{"ran":0,"of":2,"n_ran_checked":0,"n_instrument":0,"unverified":2,"pointer_only":0,"phrase":"0 ran · 2 unverified","official":{"repos":["eydcao/bsms-gnn"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":[]}}},{"paper":"/paper/particle-clustering-in-turbulence-prediction","slug":"particle-clustering-in-turbulence-prediction","title":"Particle clustering in turbulence: Prediction of spatial and statistical properties with deep learning","date":"2022-10-05","arxiv_id":"2210.02339","n_code_links":1,"syntology":null},{"paper":null,"slug":"priornet-lesion-segmentation-in-pet-ct","title":"PriorNet: lesion segmentation in PET-CT including prior tumor appearance information","date":"2022-10-05","arxiv_id":"2210.02203","n_code_links":0,"syntology":null},{"paper":null,"slug":"tc-sknet-with-gridmask-for-low-complexity","title":"TC-SKNet with GridMask for Low-complexity Classification of Acoustic scene","date":"2022-10-05","arxiv_id":"2210.02287","n_code_links":0,"syntology":null},{"paper":"/paper/analysis-of-the-performance-of-u-net-neural","slug":"analysis-of-the-performance-of-u-net-neural","title":"Analysis of the performance of U-Net neural networks for the segmentation of living cells","date":"2022-10-04","arxiv_id":"2210.01538","n_code_links":1,"syntology":null},{"paper":null,"slug":"anatomically-constrained-ct-image-translation","title":"Anatomically constrained CT image translation for heterogeneous blood vessel segmentation","date":"2022-10-04","arxiv_id":"2210.01713","n_code_links":0,"syntology":null},{"paper":null,"slug":"automated-medical-device-display-reading","title":"Automated Medical Device Display Reading Using Deep Learning Object Detection","date":"2022-10-04","arxiv_id":"2210.01325","n_code_links":0,"syntology":null},{"paper":null,"slug":"invariant-aggregator-for-defending-federated","title":"Invariant Aggregator for Defending against Federated Backdoor Attacks","date":"2022-10-04","arxiv_id":"2210.01834","n_code_links":0,"syntology":null},{"paper":null,"slug":"towards-flexible-inductive-bias-via","title":"Towards Flexible Inductive Bias via Progressive Reparameterization Scheduling","date":"2022-10-04","arxiv_id":"2210.01370","n_code_links":0,"syntology":null},{"paper":null,"slug":"feature-embedding-by-template-matching-as-a","title":"Feature Embedding by Template Matching as a ResNet Block","date":"2022-10-03","arxiv_id":"2210.00992","n_code_links":0,"syntology":null},{"paper":null,"slug":"multipod-convolutional-network","title":"Multipod Convolutional Network","date":"2022-10-03","arxiv_id":"2210.00689","n_code_links":0,"syntology":null},{"paper":null,"slug":"wavefit-an-iterative-and-non-autoregressive","title":"WaveFit: An Iterative and Non-autoregressive Neural Vocoder based on Fixed-Point Iteration","date":"2022-10-03","arxiv_id":"2210.01029","n_code_links":0,"syntology":null},{"paper":"/paper/siamese-nas-using-trained-samples-efficiently","slug":"siamese-nas-using-trained-samples-efficiently","title":"Siamese-NAS: Using Trained Samples Efficiently to Find Lightweight Neural Architecture by Prior Knowledge","date":"2022-10-02","arxiv_id":"2210.00546","n_code_links":1,"syntology":null},{"paper":null,"slug":"attention-augmented-convnext-unet-for-rectal","title":"Attention Augmented ConvNeXt UNet For Rectal Tumour Segmentation","date":"2022-10-01","arxiv_id":"2210.00227","n_code_links":0,"syntology":null},{"paper":"/paper/det-slam-a-semantic-visual-slam-for-highly","slug":"det-slam-a-semantic-visual-slam-for-highly","title":"Det-SLAM: A semantic visual SLAM for highly dynamic scenes using Detectron2","date":"2022-10-01","arxiv_id":"2210.00278","n_code_links":1,"syntology":null},{"paper":null,"slug":"eapruning-evolutionary-pruning-for-vision","title":"EAPruning: Evolutionary Pruning for Vision Transformers and CNNs","date":"2022-10-01","arxiv_id":"2210.00181","n_code_links":0,"syntology":null},{"paper":null,"slug":"kernel-normalized-convolutional-networks-for","title":"Kernel Normalized Convolutional Networks for Privacy-Preserving Machine Learning","date":"2022-09-30","arxiv_id":"2210.00053","n_code_links":0,"syntology":null},{"paper":"/paper/rethinking-skip-connection-model-as-a","slug":"rethinking-skip-connection-model-as-a","title":"Rethinking skip connection model as a learnable Markov chain","date":"2022-09-30","arxiv_id":"2209.15278","n_code_links":1,"syntology":null},{"paper":"/paper/towards-multi-spatiotemporal-scale","slug":"towards-multi-spatiotemporal-scale","title":"Towards Multi-spatiotemporal-scale Generalized PDE Modeling","date":"2022-09-30","arxiv_id":"2209.15616","n_code_links":2,"syntology":null},{"paper":null,"slug":"where-should-i-spend-my-flops-efficiency","title":"Where Should I Spend My FLOPS? Efficiency Evaluations of Visual Pre-training Methods","date":"2022-09-30","arxiv_id":"2209.15589","n_code_links":0,"syntology":null},{"paper":null,"slug":"creative-painting-with-latent-diffusion","title":"Creative Painting with Latent Diffusion Models","date":"2022-09-29","arxiv_id":"2209.14697","n_code_links":0,"syntology":null},{"paper":null,"slug":"eihi-net-out-of-distribution-generalization","title":"EiHi Net: Out-of-Distribution Generalization Paradigm","date":"2022-09-29","arxiv_id":"2209.14946","n_code_links":0,"syntology":null},{"paper":"/paper/facial-landmark-predictions-with-applications","slug":"facial-landmark-predictions-with-applications","title":"Facial Landmark Predictions with Applications to Metaverse","date":"2022-09-29","arxiv_id":"2209.14698","n_code_links":1,"syntology":null},{"paper":"/paper/make-a-video-text-to-video-generation-without","slug":"make-a-video-text-to-video-generation-without","title":"Make-A-Video: Text-to-Video Generation without Text-Video Data","date":"2022-09-29","arxiv_id":"2209.14792","n_code_links":2,"syntology":null}],"record_sha256":"906fe0c288bd506f061ac8fbcf9ef6f6d59a43714196536fc7eae0c3b2a93c6d","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}