1.2k citations · 4.5k across the 78 of their papers we have counts for
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Quantity vs. Quality: On Hyperparameter Optimization for Deep Reinforcement Learning
Lars Hertel, Pierre Baldi, Daniel L. Gillen
Reinforcement learning algorithms can show strong variation in performance between training runs with different random seeds. In this paper we explore how this affects hyperparamet…
SPLASH: Learnable Activation Functions for Improving Accuracy and Adversarial Robustness
Mohammadamin Tavakoli, Forest Agostinelli, Pierre Baldi
We introduce SPLASH units, a class of learnable activation functions shown to simultaneously improve the accuracy of deep neural networks while also improving their robustness to a…
Sherpa: Robust Hyperparameter Optimization for Machine Learning
Lars Hertel, Julian Collado, Peter Sadowski +2
Sherpa is a hyperparameter optimization library for machine learning models. It is specifically designed for problems with computationally expensive, iterative function evaluations…
Continuous Representation of Molecules Using Graph Variational Autoencoder
Mohammadamin Tavakoli, Pierre Baldi
In order to continuously represent molecules, we propose a generative model in the form of a VAE which is operating on the 2D-graph structure of molecules. A side predictor is empl…
A Fortran-Keras Deep Learning Bridge for Scientific Computing
Jordan Ott, Mike Pritchard, Natalie Best +3
Implementing artificial neural networks is commonly achieved via high-level programming languages like Python and easy-to-use deep learning libraries like Keras. These software lib…