6.9k citations · 9k across the 31 of their papers we have counts for
5 papers · 1 filter
Unbiased Gradient Estimation in Unrolled Computation Graphs with Persistent Evolution Strategies
Paul Vicol, Luke Metz, Jascha Sohl-Dickstein
Unrolled computation graphs arise in many scenarios, including training RNNs, tuning hyperparameters through unrolled optimization, and training learned optimizers. Current approac…
Lyapunov Exponents for Diversity in Differentiable Games
Jonathan Lorraine, Paul Vicol, Jack Parker-Holder +3
Ridge Rider (RR) is an algorithm for finding diverse solutions to optimization problems by following eigenvectors of the Hessian ("ridges"). RR is designed for conservative gradien…
Gradients are Not All You Need
Luke Metz, C. Daniel Freeman, Samuel S. Schoenholz +1
Differentiable programming techniques are widely used in the community and are responsible for the machine learning renaissance of the past several decades. While these methods are…
Learn2Hop: Learned Optimization on Rough Landscapes
Amil Merchant, Luke Metz, Sam Schoenholz +1
Optimization of non-convex loss surfaces containing many local minima remains a critical problem in a variety of domains, including operations research, informatics, and material d…
Training Learned Optimizers with Randomly Initialized Learned Optimizers
Luke Metz, C. Daniel Freeman, Niru Maheswaranathan +1
Learned optimizers are increasingly effective, with performance exceeding that of hand designed optimizers such as Adam~\citep{kingma2014adam} on specific tasks \citep{metz2019unde…