Search Results for author:
Found 4 papers, 3 papers with code
On the symmetries in the dynamics of wide two-layer neural networks
We consider the idealized setting of gradient flow on the population risk for infinitely wide two-layer ReLU neural networks (without bias), and study the effect of symmetries on the learned parameters and predictors.
Training Integrable Parameterizations of Deep Neural Networks in the Infinite-Width Limit
For two-layer neural networks, it has been understood via these asymptotics that the nature of the trained model radically changes depending on the scale of the initial random weights, ranging from a kernel regime (for large initial variance) to a feature learning regime (for small initial variance).
SizeFlags: Reducing Size and Fit Related Returns in Fashion E-Commerce
Size and fit related returns severely impact 1. the customers experience and their dissatisfaction with online shopping, 2. the environment through an increased carbon footprint, and 3. the profitability of online fashion platforms.
Ranked Reward: Enabling Self-Play Reinforcement Learning for Combinatorial Optimization
Results from applying the R2 algorithm to instances of a two-dimensional and three-dimensional bin packing problems show that it outperforms generic Monte Carlo tree search, heuristic algorithms and integer programming solvers.
