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Found 7 papers, 4 papers with code
Graph Neural Networks and Applied Linear Algebra
With the recent interest in scientific machine learning, it is natural to ask how sparse matrix computations can leverage neural networks (NN).
Parallel Training of GRU Networks with a Multi-Grid Solver for Long Sequences
Parallelizing Gated Recurrent Unit (GRU) networks is a challenging task, as the training procedure of GRU is inherently sequential.
Partition of unity networks: deep hp-approximation
Approximation theorists have established best-in-class optimal approximation rates of deep neural networks by utilizing their ability to simultaneously emulate partitions of unity and monomials.
A physics-informed operator regression framework for extracting data-driven continuum models
The application of deep learning toward discovery of data-driven models requires careful application of inductive biases to obtain a description of physics which is both accurate and robust.
A block coordinate descent optimizer for classification problems exploiting convexity
By alternating between a second-order method to find globally optimal parameters for the linear layer and gradient descent to train the hidden layers, we ensure an optimal fit of the adaptive basis to data throughout training.
Multilevel Initialization for Layer-Parallel Deep Neural Network Training
This paper investigates multilevel initialization strategies for training very deep neural networks with a layer-parallel multigrid solver.
Robust Training and Initialization of Deep Neural Networks: An Adaptive Basis Viewpoint
Motivated by the gap between theoretical optimal approximation rates of deep neural networks (DNNs) and the accuracy realized in practice, we seek to improve the training of DNNs.
