Independent Component Analysis

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A Sober Look at the Unsupervised Learning of Disentangled Representations and their Evaluation (2020, methodology): “Similarly, the non-linear ICA community recently shifted to non-iid data types exploiting time dependent or grouped observations (Hyvarinen and Morioka, 2016; Hyvarinen et al., 2019; Gresele et al., 2019) We focus our study on the setting where factors of variations are not observable at all, that i”
Contrastive Multiview Coding (2019, methodology): “This field has a long history, starting with classical methods with well established algorithms, such as principal components analysis (PCA [29]) and independent components analysis (ICA [26]).”
Flow++: Improving Flow-Based Generative Models with Variational Dequantization and Architecture Design (2019, methodology): “A flow model f is constructed as an invertible transformation that maps observed data x to a standard Gaussian latent variable z = f(x), as in nonlinear independent component analysis (Bell & Sejnowski, 1995; Hyvärinen et al., 2004; Hyvärinen & Pajunen, 1999).”
Density estimation using Real NVP (2016, background): “Also, this ﬂexibility will help us leverage recent advances in batch normalization [25] and residual networks [19, 20].”
Rethinking LDA: Moment Matching for Discrete ICA (2015, neutral): “, [17, 18, 19, 20, 21, 5] and references therein) and machine learning (see [3, 4] and references therein) literature.”