https://hackerpaper.kaipfister.de/p/1410.8516/Collected HN comments and submissions linking this paper.enMon, 02 Nov 2026 11:16:22 GMThttps://news.ycombinator.com/item?id=41869632urn:hackerpapers:mention:1410.8516:41869632Thu, 17 Oct 2024 13:46:52 GMT<p>Defer to other experts, but (briefly) normalizing flows are a method for constructing complex distributions by transforming a probability density through a series of invertible transformations. Normalizing flows are trained using a plain log-likelihood function, and they are capable of exact density evaluation and efficient sampling. See: Danilo Rezende and Shakir Mohamed. Variational inference with normalizing flows. In ICML, 2015. Link: https://bigdata.duke.edu/wp-content/uploads/2022/08/1505.057... Laurent Dinh, David Krueger, and Yoshua Bengio. Nice: Non-linear independent components…</p><p><a href="https://hackerpaper.kaipfister.de/p/1410.8516/">Paper and discussion history</a>. A mention is not an endorsement.</p>cs.LGhttps://news.ycombinator.com/item?id=18198486urn:hackerpapers:mention:1410.8516:18198486Fri, 12 Oct 2018 01:45:45 GMT<p>These guys have done what looks like an impressive amount of work... but I was disappointed to see there&#x27;s no mention of generative language modeling at all and only a very brief mention of key advances in image generation over the past few years. Examples of generative language modeling ignored by this paper (not even mentioned): * Unsupervised Transformer - https://blog.openai.com/language-unsupervised/ * ELMo - https://arxiv.org/abs/1802.05365 * ULMFit - https://arxiv.org/abs/1801.06146 Examples of generative image modeling ignored by this paper (some are mentioned only in passing): * Glow…</p><p><a href="https://hackerpaper.kaipfister.de/p/1410.8516/">Paper and discussion history</a>. A mention is not an endorsement.</p>cs.LG