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11/24/2022
Towards Practical Control of Singular Values of Convolutional Layers
In general, convolutional neural networks (CNNs) are easy to train, but ...
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05/09/2022
Tensor rank bounds and explicit QTT representations for the inverses of circulant matrices
In this paper, we are concerned with the inversion of circulant matrices...
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05/29/2021
Cherry-Picking Gradients: Learning Low-Rank Embeddings of Visual Data via Differentiable Cross-Approximation
We propose an end-to-end trainable framework that processes large-scale ...
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03/27/2021
Automatic differentiation for Riemannian optimization on low-rank matrix and tensor-train manifolds
In scientific computing and machine learning applications, matrices and ...
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03/07/2021
Spectral Tensor Train Parameterization of Deep Learning Layers
We study low-rank parameterizations of weight matrices with embedded spe...
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10/14/2020
Low rank tensor approximation of singularly perturbed partial differential equations in one dimension
We derive rank bounds on the quantized tensor train (QTT) compressed app...
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07/13/2020
T-Basis: a Compact Representation for Neural Networks
We introduce T-Basis, a novel concept for a compact representation of a ...
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12/17/2019
Tensor Rank bounds for Point Singularities in R^3
We analyze rates of approximation by quantized, tensor-structured repres...
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04/05/2017
