An information-maximization approach to blind separation and blind deconvolution

1996

Citations in the corpus

• 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; Hyv¨arinen et al., 2004; Hyv¨arinen & Pajunen, 1999).”
• On Variational Bounds of Mutual Information (2019, background): “…x ∼ p ( x ) , the goal is to learn a stochastic representation of the data p θ ( y | x ) that has maximal MI with X subject to constraints on the mapping (e.g. Bell & Sejnowski, 1995; Krause et al., 2010; Hu et al., 2017; van den Oord et al., 2018; Hjelm et al., 2018; Alemi et al., 2017).”
• High-Dimensional Probability Estimation with Deep Density Models (2013, methodology): “Independent components analysis [Bell and Sejnowski, 1995] and the related idea of a density network [MacKay and Gibbs, 1997] are examples of bijective models that exploit invertible linear transformations; these, however, have rather limited expressiveness.”
• Formal Limitations on the Measurement of Mutual Information (2018, methodology): “Another related framework is INFOMAX (Linsker, 1988; Bell & Sejnowski, 1995; Hjelm et al., 2018).”
• Maximum Entropy Generators for Energy-Based Models (2019, background): “With x = G ( z ) the function of interest and x discrete-valued: This idea of maximizing mutual information between inputs and outputs of an encoder to maximize the entropy of its output dates back at least to Linsker (1988); Bell & Sejnowski (1995) and their work on Infomax.”
• Underdetermined blind source separation using sparse representations (2001, methodology): “One of the most popular ICA approaches is the InfoMax algorithm [1] that maximizes the information of the recovered sources.”
• $rm K$-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation (2006, methodology): “There is an interesting relation between the above method and the independent component analysis (ICA) algorithm [43].”
• A review of group ICA for fMRI data and ICA for joint inference of imaging, genetic, and ERP data (2008, unknown): “Furthermore, since the proto-typical confounds are also sparse and localized, e.g., vascular pulsation (signal localized to larger veins that are moving as a result of cardiac pulsation) or breathing induced motion (signal localized to strong tissue contrast near discontinuities: qtissue edgesq), th”
• Supervised Speech Separation Based on Deep Learning: An Overview (2017, unknown): “formation include beamforming, as mentioned in Section I, and 1153 independent component analysis [3], [8], [85].”
• Evolving Deep LSTM-based Memory Networks using an Information Maximization Objective (2016, methodology): “This information-theoretic objective to train LSTM network is similar to the info-max approach published in [6].”
• Optimal kernels for unsupervised learning (2005, unknown): “EXPERIMENTS: INDEPENDENTCOMPONENT ANALYSIS Here we apply our new sample-based method to independent component analysis (ICA [5], [1], [2]) in a framework depicted in Fig.”
• Probabilistic independent component analysis for functional magnetic resonance imaging (2004, methodology): “In [36], the sources are estimated by iteratively optimising an unmixing matrix W = A−1 so that S = WX contains mutually independent rows, using the infomax algorithm [9].”
• Motor patterns in human walking and running. (2006, methodology): “As before, we ordered the ICA and NMF components so that their waveforms matched the corresponding FA components (Fig.”
• THE CO-INFORMATION LATTICE (2003, background): “In Independent Component Analysis (ICA) and its emerging generalisation, Dependent Component Analysis (DCA), we are looking for a linear transform, u = Wx, of the random vector, x, such that, again, the KLdivergence , D[p(x)||p G (x)] = H G (x) − H(x), is min- imised.”
• Independent Component Analysis (2005, unknown): “ICA has been applied to separation of different speech signals [1] 2 , analysis of EEG data [6], functional magnetic resonance imaging (fMRI) data [7], image processing [2], and as a model of biological image processing [10].”
• Advances in Nonlinear Blind Source Separation (2003, methodology): “Sin ce 1985 (see [5] for a historical review and early references), several well-performing BSS and ICA algorithms [6], [7], [8 ], [9], [10], [11], [12] have been developed and applied to an increasing number of applications [13], [14], [15], [16], [ 17], [1], [18], [4].”