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Found 10 papers, 0 papers with code
Spectral Learning on Matrices and Tensors
PCA and other spectral techniques applied to matrices have several limitations.
Beating the Perils of Non-Convexity: Guaranteed Training of Neural Networks using Tensor Methods
We propose a novel algorithm based on tensor decomposition for guaranteed training of two-layer neural networks.
Score Function Features for Discriminative Learning
In this paper, we consider a novel class of matrix and tensor-valued features, which can be pre-trained using unlabeled samples.
Score Function Features for Discriminative Learning: Matrix and Tensor Framework
In this paper, we consider a novel class of matrix and tensor-valued features, which can be pre-trained using unlabeled samples.
Provable Tensor Methods for Learning Mixtures of Generalized Linear Models
In contrast, we present a tensor decomposition method which is guaranteed to correctly recover the parameters.
Analyzing Tensor Power Method Dynamics in Overcomplete Regime
We present a novel analysis of the dynamics of tensor power iterations in the overcomplete regime where the tensor CP rank is larger than the input dimension.
Sample Complexity Analysis for Learning Overcomplete Latent Variable Models through Tensor Methods
In the unsupervised setting, we use a simple initialization algorithm based on SVD of the tensor slices, and provide guarantees under the stricter condition that $k\le \beta d$ (where constant $\beta$ can be larger than $1$), where the tensor method recovers the components under a polynomial running time (and exponential in $\beta$).
Guaranteed Non-Orthogonal Tensor Decomposition via Alternating Rank-$1$ Updates
In this paper, we provide local and global convergence guarantees for recovering CP (Candecomp/Parafac) tensor decomposition.
When are Overcomplete Topic Models Identifiable? Uniqueness of Tensor Tucker Decompositions with Structured Sparsity
This set of higher-order expansion conditions allow for overcomplete models, and require the existence of a perfect matching from latent topics to higher order observed words.
High-Dimensional Covariance Decomposition into Sparse Markov and Independence Models
Fitting high-dimensional data involves a delicate tradeoff between faithful representation and the use of sparse models.
