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Found 7 papers, 2 papers with code
Regularized Gauss-Newton for Optimizing Overparameterized Neural Networks
This work studies a GGN method for optimizing a two-layer neural network with explicit regularization.
Efficient Learning Using Spiking Neural Networks Equipped With Affine Encoders and Decoders
We study the learning problem associated with spiking neural networks.
Mathematical Capabilities of ChatGPT
We investigate the mathematical capabilities of two iterations of ChatGPT (released 9-January-2023 and 30-January-2023) and of GPT-4 by testing them on publicly available datasets, as well as hand-crafted ones, using a novel methodology.
VC dimensions of group convolutional neural networks
We study the generalization capacity of group convolutional neural networks.
Limitations of neural network training due to numerical instability of backpropagation
We study the training of deep neural networks by gradient descent where floating-point arithmetic is used to compute the gradients.
Deep neural networks can stably solve high-dimensional, noisy, non-linear inverse problems
We demonstrate the admissibility of this approach to a wide range of inverse problems of practical interest.
The Oracle of DLphi
We present a novel technique based on deep learning and set theory which yields exceptional classification and prediction results.
