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Found 7 papers, 1 papers with code
Latent Temporal Flows for Multivariate Analysis of Wearables Data
Latent Temporal Flows simultaneously recovers a transformation of the observed sequences into lower-dimensional latent representations via deep autoencoder mappings, and estimates a temporally-conditioned probabilistic model via normalizing flows.
Learning Multivariate CDFs and Copulas using Tensor Factorization
Learning the multivariate distribution of data is a core challenge in statistics and machine learning.
LatTe Flows: Latent Temporal Flows for Multivariate Sequence Analysis
We introduce Latent Temporal Flows (\emph{LatTe-Flows}), a method for probabilistic multivariate time-series analysis tailored for high dimensional systems whose temporal dynamics are driven by variations in a lower-dimensional discriminative subspace.
Low-rank Characteristic Tensor Density Estimation Part II: Compression and Latent Density Estimation
Learning generative probabilistic models is a core problem in machine learning, which presents significant challenges due to the curse of dimensionality.
Information-theoretic Feature Selection via Tensor Decomposition and Submodularity
By indirectly aiming to predict the latent variable of the naive Bayes model instead of the original target variable, it is possible to formulate the feature selection problem as maximization of a monotone submodular function subject to a cardinality constraint - which can be tackled using a greedy algorithm that comes with performance guarantees.
Low-rank Characteristic Tensor Density Estimation Part I: Foundations
Any multivariate density can be represented by its characteristic function, via the Fourier transform.
From Gene Expression to Drug Response: A Collaborative Filtering Approach
Predicting the response of cancer cells to drugs is an important problem in pharmacogenomics.
