20 citations · 31 across the 4 of their papers we have counts for
3 papers · 1 filter
LeDeepChef: Deep Reinforcement Learning Agent for Families of Text-Based Games
Leonard Adolphs, Thomas Hofmann
While Reinforcement Learning (RL) approaches lead to significant achievements in a variety of areas in recent history, natural language tasks remained mostly unaffected, due to the…
Adaptive norms for deep learning with regularized Newton methods
Jonas Kohler, Leonard Adolphs, Aurelien Lucchi
We investigate the use of regularized Newton methods with adaptive norms for optimizing neural networks. This approach can be seen as a second-order counterpart of adaptive gradien…
Local Saddle Point Optimization: A Curvature Exploitation Approach
Leonard Adolphs, Hadi Daneshmand, Aurelien Lucchi +1
Gradient-based optimization methods are the most popular choice for finding local optima for classical minimization and saddle point problems. Here, we highlight a systemic issue o…