16 citations · 21 across the 4 of their papers we have counts for
3 papers
cs.LG2021
Variational autoencoders in the presence of low-dimensional data: landscape and implicit bias
Frederic Koehler, Viraj Mehta, Chenghui Zhou +1
Variational Autoencoders are one of the most commonly used generative models, particularly for image data. A prominent difficulty in training VAEs is data that is supported on a lo…
cs.LG2016★ 5 cited
Provable learning of Noisy-or Networks
Sanjeev Arora, Rong Ge, Tengyu Ma +1
Many machine learning applications use latent variable models to explain structure in data, whereby visible variables (= coordinates of the given datapoint) are explained as a prob…
cs.LG2016★ 16 cited
Recovery Guarantee of Non-negative Matrix Factorization via Alternating Updates
Yuanzhi Li, Yingyu Liang, Andrej Risteski
Non-negative matrix factorization is a popular tool for decomposing data into feature and weight matrices under non-negativity constraints. It enjoys practical success but is poorl…