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Found 5 papers, 4 papers with code
Towards Practical Control of Singular Values of Convolutional Layers
We demonstrate the improved properties of modern CNNs with our method and analyze its impact on the model performance, calibration, and adversarial robustness.
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 visual data tensors by looking at a fraction of their entries only.
Automatic differentiation for Riemannian optimization on low-rank matrix and tensor-train manifolds
In scientific computing and machine learning applications, matrices and more general multidimensional arrays (tensors) can often be approximated with the help of low-rank decompositions.
Spectral Tensor Train Parameterization of Deep Learning Layers
We study low-rank parameterizations of weight matrices with embedded spectral properties in the Deep Learning context.
T-Basis: a Compact Representation for Neural Networks
Each of the tensors in the set is modeled using Tensor Rings, though the concept applies to other Tensor Networks.
