MenuNew:NeurIPS2021: Shared Independent Component Analysis for Multi-Subject Neuroimaging NeurIPS2021: Disentangling Identifiable Features from Noisy Data with Structured Nonlinear ICA AISTATS2021: Causal Autoregressive Flows AISTATS2021: Independent innovation analysis for nonlinear vector autoregressive process JMLR: Information criteria for non-normalized models JNE: Uncovering the Structure of Clinical EEG Signals with Self-supervised Learning |
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Software[Mostly programmed by collaborators, but implementing algorithms I have (co-)developed] Deep unsupervised learningStructured Nonlinear ICA using JAX ICE-BEeM using Pytorch iVAE using PyTorch Independent Innovation Analysis using PyTorch Nonlinear ICA using HMM using JAX Time-Contrastive Learning using TensorFlow 1 (a bit old) Linear unsupervised learningFastICA: Fast Independent Component Analysis (for Matlab, see scikit-learn for a good Python version) ICASSO: Analyzing reliability of independent components (for Matlab, see here for Python) LiNGAM: Causal discovery based on non-Gaussianity (for various systems: Python, R, Matlab) Shared Independent Component Analysis for multi-view data (in Python) Natural image statistics / visual modelling (for Matlab)Natural Image Statistics package (code for the book); Neuroimaging data analysis (for Matlab)SpeDeBox: Decoding EEG/MEG using spectral infomation OCF: Analysing variability (nonstationarity) of connectivity ISCTEST: Testing independent components Fourier-ICA: Improved ICA by time-frequency transforms |