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Leveraging recent advances in Stochastic Gradient Variational Bayes,\nDVBF can overcome intractable inference distributions via variational\ninference. Thus, it can handle highly nonlinear input data with temporal and\nspatial dependencies such as image sequences without domain knowledge. Our\nexperiments show that enabling backpropagation through transitions enforces\nstate space assumptions and significantly improves information content of the\nlatent embedding. This also enables realistic long-term prediction.","url_abs":"http://arxiv.org/abs/1605.06432v3","url_pdf":"http://arxiv.org/pdf/1605.06432v3.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":"deep-variational-bayes-filters-unsupervised","repo_url":"https://github.com/baggepinnen/DVBF.jl","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"deep-variational-bayes-filters-unsupervised","repo_url":"https://github.com/axelbr/dvbf","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"deep-variational-bayes-filters-unsupervised","repo_url":"https://github.com/gregorsemmler/pytorch-dvbf","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"deep-variational-bayes-filters-unsupervised","repo_url":"https://github.com/baggepinnen/DeepFilters.jl","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"state-space-models","task_name":"State Space Models"},{"task_slug":"variational-inference","task_name":"Variational Inference"}],"methods":[{"method_slug":"stochastic-gradient-variational-bayes","method_name":"Stochastic Gradient Variational Bayes"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1605.06432","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1605.06432"}},"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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