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Found 4 papers, 1 papers with code
The Dynamics of Gradient Descent for Overparametrized Neural Networks
We consider the dynamics of gradient descent (GD) in overparameterized single hidden layer neural networks with a squared loss function.
Sample Complexity and Overparameterization Bounds for Temporal Difference Learning with Neural Network Approximation
In this paper, we study the dynamics of temporal difference learning with neural network-based value function approximation over a general state space, namely, \emph{Neural TD learning}.
Learning Latent Events from Network Message Logs
One of the main contributions of the paper is a novel mapping of our problem which transforms it into a problem of topic discovery in documents.
Group-Sparse Model Selection: Hardness and Relaxations
Group-based sparsity models are proven instrumental in linear regression problems for recovering signals from much fewer measurements than standard compressive sensing.
