{"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/on-the-challenges-of-learning-with-inference","title":"On the challenges of learning with inference networks on sparse, high-dimensional data","arxiv_id":"1710.06085","date":"2017-10-17","proceeding":null,"authors":["Rahul G. Krishnan","Dawen Liang","Matthew Hoffman"],"abstract":"We study parameter estimation in Nonlinear Factor Analysis (NFA) where the\ngenerative model is parameterized by a deep neural network. Recent work has\nfocused on learning such models using inference (or recognition) networks; we\nidentify a crucial problem when modeling large, sparse, high-dimensional\ndatasets -- underfitting. We study the extent of underfitting, highlighting\nthat its severity increases with the sparsity of the data. We propose methods\nto tackle it via iterative optimization inspired by stochastic variational\ninference \\citep{hoffman2013stochastic} and improvements in the sparse data\nrepresentation used for inference. The proposed techniques drastically improve\nthe ability of these powerful models to fit sparse data, achieving\nstate-of-the-art results on a benchmark text-count dataset and excellent\nresults on the task of top-N recommendation.","url_abs":"http://arxiv.org/abs/1710.06085v1","url_pdf":"http://arxiv.org/pdf/1710.06085v1.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":"on-the-challenges-of-learning-with-inference","repo_url":"https://github.com/rahulk90/vae_sparse","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"variational-inference","task_name":"Variational Inference"},{"task_slug":"parameter-estimation","task_name":"parameter estimation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1710.06085","atlas_url":"https://app.syntology.ai/?focus=1710.06085","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}