{"about":{"site":"https://codewithpapers.app","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.","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"},"url":"/paper/sparse-activity-and-sparse-connectivity-in","title":"Sparse Activity and Sparse Connectivity in Supervised Learning","arxiv_id":"1603.08367","date":"2016-03-28","proceeding":null,"authors":["Markus Thom","Günther Palm"],"abstract":"Sparseness is a useful regularizer for learning in a wide range of\napplications, in particular in neural networks. This paper proposes a model\ntargeted at classification tasks, where sparse activity and sparse connectivity\nare used to enhance classification capabilities. The tool for achieving this is\na sparseness-enforcing projection operator which finds the closest vector with\na pre-defined sparseness for any given vector. In the theoretical part of this\npaper, a comprehensive theory for such a projection is developed. In\nconclusion, it is shown that the projection is differentiable almost everywhere\nand can thus be implemented as a smooth neuronal transfer function. The entire\nmodel can hence be tuned end-to-end using gradient-based methods. Experiments\non the MNIST database of handwritten digits show that classification\nperformance can be boosted by sparse activity or sparse connectivity. With a\ncombination of both, performance can be significantly better compared to\nclassical non-sparse approaches.","url_abs":"http://arxiv.org/abs/1603.08367v1","url_pdf":"http://arxiv.org/pdf/1603.08367v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"image-classification","task_name":"Image Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/image-classification-on-mnist","task":"Image Classification","dataset":"MNIST","model":"Sparse Activity and Sparse Connectivity in Supervised Learning","rank_in_archive_order":45,"of":81,"metrics":{"Percentage error":"0.8"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1603.08367","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}