{"url":"/task/channel-selection","name":"channel selection","slug":"channel-selection","description_markdown":null,"categories":[{"name":"Medical","url":"/area/medical"},{"name":"Time Series","url":"/area/time-series"}],"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","slug_source":"derived"},"counts":{"papers_tagged":119,"papers_with_code":27,"benchmarks":0,"benchmark_tables_in_archive":0,"benchmark_tables_shown":0,"benchmark_tables_withheld_as_spam":0,"benchmark_definition":"a leaderboard table with at least one row; benchmark_tables_shown also counts the zero-row tables; benchmark_tables_in_archive adds the tables withheld as spam","datasets":0,"subtasks":0,"parent_tasks":1},"benchmarks":[],"datasets":[],"subtasks":[],"parent_tasks":[{"url":"/task/brain-computer-interface","name":"Brain Computer Interface"}],"papers":{"order":"repositories listed in the archive (desc), then date (desc); the archive holds no stars","population":"papers tagged with this task that list at least one repository in the archive","shown":27,"of":27,"tagged_in_all":119,"items":[{"url":"/paper/learning-in-the-frequency-domain","title":"Learning in the Frequency Domain","date":"2020-02-27","arxiv_id":"2002.12416","repositories_listed":4,"syntology":{"n":1,"n_ran":1,"n_unverified":0,"n_pointer_only":1}},{"url":"/paper/neuroxai-adaptive-robust-explainable","title":"NeuroXAI: Adaptive, robust, explainable surrogate framework for determination of channel importance in EEG application","date":"2025-09-12","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/sepprune-structured-pruning-for-efficient","title":"SepPrune: Structured Pruning for Efficient Deep Speech Separation","date":"2025-05-17","arxiv_id":"2505.12079","repositories_listed":1,"syntology":null},{"url":"/paper/differentiable-channel-selection-in-self","title":"Differentiable Channel Selection in Self-Attention For Person Re-Identification","date":"2025-05-13","arxiv_id":"2505.08961","repositories_listed":1,"syntology":null},{"url":"/paper/slimseiz-efficient-channel-adaptive-seizure","title":"SlimSeiz: Efficient Channel-Adaptive Seizure Prediction Using a Mamba-Enhanced Network","date":"2024-10-13","arxiv_id":"2410.09998","repositories_listed":1,"syntology":null},{"url":"/paper/learning-binary-color-filter-arrays-with","title":"Learning Binary Color Filter Arrays with Trainable Hard Thresholding","date":"2024-06-20","arxiv_id":"2406.14421","repositories_listed":1,"syntology":null},{"url":"/paper/improving-the-evaluation-and-actionability-of","title":"Improving the Evaluation and Actionability of Explanation Methods for Multivariate Time Series Classification","date":"2024-06-18","arxiv_id":"2406.12507","repositories_listed":1,"syntology":null},{"url":"/paper/quantifying-spatial-domain-explanations-in","title":"Quantifying Spatial Domain Explanations in BCI using Earth Mover's Distance","date":"2024-05-02","arxiv_id":"2405.01277","repositories_listed":1,"syntology":null},{"url":"/paper/hcf-net-hierarchical-context-fusion-network","title":"HCF-Net: Hierarchical Context Fusion Network for Infrared Small Object Detection","date":"2024-03-16","arxiv_id":"2403.10778","repositories_listed":1,"syntology":null},{"url":"/paper/filter-pruning-for-cnn-with-enhanced-linear","title":"Filter Pruning For CNN With Enhanced Linear Representation Redundancy","date":"2023-10-10","arxiv_id":"2310.06344","repositories_listed":1,"syntology":null},{"url":"/paper/unifying-and-personalizing-weakly-supervised","title":"Unifying and Personalizing Weakly-supervised Federated Medical Image Segmentation via Adaptive Representation and Aggregation","date":"2023-04-12","arxiv_id":"2304.05635","repositories_listed":1,"syntology":null},{"url":"/paper/testing-the-channels-of-convolutional-neural","title":"Testing the Channels of Convolutional Neural Networks","date":"2023-03-06","arxiv_id":"2303.03400","repositories_listed":1,"syntology":null},{"url":"/paper/dynamic-channel-selection-in-self-supervised","title":"Dynamic Channel Selection in Self-Supervised Learning","date":"2022-07-25","arxiv_id":"2207.12065","repositories_listed":1,"syntology":null},{"url":"/paper/personalizing-federated-medical-image","title":"Personalizing Federated Medical Image Segmentation via Local Calibration","date":"2022-07-11","arxiv_id":"2207.04655","repositories_listed":1,"syntology":{"n":11,"n_ran":9,"n_unverified":2,"n_pointer_only":11}},{"url":"/paper/improving-motor-imagery-eeg-classification","title":"Improving Motor Imagery EEG Classification Based on Channel Selection Using a Deep Learning Architecture","date":"2022-07-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/scalable-classifier-agnostic-channel","title":"Scalable Classifier-Agnostic Channel Selection for Multivariate Time Series Classification","date":"2022-06-18","arxiv_id":"2206.09274","repositories_listed":1,"syntology":null},{"url":"/paper/omni-frequency-channel-selection","title":"Omni-frequency Channel-selection Representations for Unsupervised Anomaly Detection","date":"2022-03-01","arxiv_id":"2203.00259","repositories_listed":1,"syntology":{"n":8,"n_ran":0,"n_unverified":8,"n_pointer_only":0}},{"url":"/paper/auto-compressing-subset-pruning-for-semantic","title":"Auto-Compressing Subset Pruning for Semantic Image Segmentation","date":"2022-01-26","arxiv_id":"2201.11103","repositories_listed":1,"syntology":null},{"url":"/paper/multiple-time-series-fusion-based-on-lstm-an","title":"Multiple Time Series Fusion Based on LSTM An Application to CAP A Phase Classification Using EEG","date":"2021-12-18","arxiv_id":"2112.11218","repositories_listed":1,"syntology":null},{"url":"/paper/learning-to-rank-microphones-for-distant","title":"Learning to Rank Microphones for Distant Speech Recognition","date":"2021-04-06","arxiv_id":"2104.02819","repositories_listed":1,"syntology":null},{"url":"/paper/towards-optimal-filter-pruning-with-balanced","title":"Towards Optimal Filter Pruning with Balanced Performance and Pruning Speed","date":"2020-10-14","arxiv_id":"2010.06821","repositories_listed":1,"syntology":null},{"url":"/paper/multi-channel-attention-selection-gans-for","title":"Multi-Channel Attention Selection GANs for Guided Image-to-Image Translation","date":"2020-02-03","arxiv_id":"2002.01048","repositories_listed":1,"syntology":null},{"url":"/paper/joint-group-feature-selection-and","title":"Joint Group Feature Selection and Discriminative Filter Learning for Robust Visual Object Tracking","date":"2019-07-30","arxiv_id":"1907.13242","repositories_listed":1,"syntology":null},{"url":"/paper/deep-networks-with-probabilistic-gates","title":"Channel selection using Gumbel Softmax","date":"2018-12-11","arxiv_id":"1812.04180","repositories_listed":1,"syntology":null},{"url":"/paper/discrimination-aware-channel-pruning-for-deep","title":"Discrimination-aware Channel Pruning for Deep Neural Networks","date":"2018-10-28","arxiv_id":"1810.11809","repositories_listed":1,"syntology":null},{"url":"/paper/channel-pruning-for-accelerating-very-deep","title":"Channel Pruning for Accelerating Very Deep Neural Networks","date":"2017-07-19","arxiv_id":"1707.06168","repositories_listed":1,"syntology":null},{"url":"/paper/rough-set-based-color-channel-selection","title":"Rough Set Based Color Channel Selection","date":"2016-11-03","arxiv_id":"1611.00931","repositories_listed":1,"syntology":null}],"syntology_records":3,"syntology_note":"a paper without a record is not a recorded non-run: it may lack an arXiv id or simply be absent from the graph layer"},"description_links":{"kept":0,"unwrapped_to_text":0,"bare_urls_linked":0,"relative_images_dropped":0,"rule":"internal links are kept only when the target slug exists in the catalog"},"syntology":{"read_at":"2026-09-24T18:15:14+00:00","claim":"Per-sample execution status on synthesized fixtures ('ran N of M samples'); not a correctness claim and not a ranking signal.","status_vocabulary":{"ran_honours":"ran, honoured the contract we drafted","ran_violates":"ran, violated the contract we drafted","ran_draft_wrong":"ran; our contract draft was wrong, not the code","ran_fixture":"ran; our fixture could not drive it","ran":"ran on a synthesized input","unverified":"unverified (harvested, no recorded run)"}},"not_shown":{"libraries":"the archive has no per-task library table","trend_sparklines":"the Trend column of the benchmarks table was a rendered image; it is not in the archive","social_and_latest_sorts":"stars and social signals are not in the archive"}}