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Although specific domain knowledge can be\nused to help design representations, learning with generic priors can also be\nused, and the quest for AI is motivating the design of more powerful\nrepresentation-learning algorithms implementing such priors. This paper reviews\nrecent work in the area of unsupervised feature learning and deep learning,\ncovering advances in probabilistic models, auto-encoders, manifold learning,\nand deep networks. This motivates longer-term unanswered questions about the\nappropriate objectives for learning good representations, for computing\nrepresentations (i.e., inference), and the geometrical connections between\nrepresentation learning, density estimation and manifold learning.","url_abs":"http://arxiv.org/abs/1206.5538v3","url_pdf":"http://arxiv.org/pdf/1206.5538v3.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":[{"paper_slug":"representation-learning-a-review-and-new","repo_url":"https://github.com/AishwaryaHB/aishwaryahb.github.io","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"representation-learning-a-review-and-new","repo_url":"https://github.com/Kismuz/crypto_spread_test","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}},{"paper_slug":"representation-learning-a-review-and-new","repo_url":"https://github.com/clvrai/representation-learning-by-learning-to-count","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}},{"paper_slug":"representation-learning-a-review-and-new","repo_url":"https://github.com/gitlimlab/Representation-Learning-by-Learning-to-Count","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}},{"paper_slug":"representation-learning-a-review-and-new","repo_url":"https://github.com/rongtao-xu/representationlearning","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"representation-learning-a-review-and-new","repo_url":"https://github.com/saromanov/godownload","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"density-estimation","task_name":"Density Estimation"},{"task_slug":"representation-learning","task_name":"Representation Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1206.5538","atlas_url":"https://app.syntology.ai/?focus=1206.5538","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1206.5538"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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