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They leverage the probability product rule\nand a weight sharing scheme inspired from restricted Boltzmann machines, to\nyield an estimator that is both tractable and has good generalization\nperformance. We discuss how they achieve competitive performance in modeling\nboth binary and real-valued observations. We also present how deep NADE models\ncan be trained to be agnostic to the ordering of input dimensions used by the\nautoregressive product rule decomposition. Finally, we also show how to exploit\nthe topological structure of pixels in images using a deep convolutional\narchitecture for NADE.","url_abs":"http://arxiv.org/abs/1605.02226v3","url_pdf":"http://arxiv.org/pdf/1605.02226v3.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":"neural-autoregressive-distribution-estimation","repo_url":"https://github.com/JoonyoungYi/CFNADE-keras","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"neural-autoregressive-distribution-estimation","repo_url":"https://github.com/MarcCote/NADE","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"neural-autoregressive-distribution-estimation","repo_url":"https://github.com/EugenHotaj/pytorch-generative/blob/master/pytorch_generative/models/autoregressive/nade.py","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"density-estimation","task_name":"Density Estimation"},{"task_slug":"image-generation","task_name":"Image Generation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1605.02226","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1605.02226"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+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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