45 citations · 191 across the 17 of their papers we have counts for
17 papers
Transformer-Based Learned Optimization
Erik Gärtner, Luke Metz, Mykhaylo Andriluka +2
We propose a new approach to learned optimization where we represent the computation of an optimizer's update step using a neural network. The parameters of the optimizer are then…
VeLO: Training Versatile Learned Optimizers by Scaling Up
Luke Metz, James Harrison, C. Daniel Freeman +8
While deep learning models have replaced hand-designed features across many domains, these models are still trained with hand-designed optimizers. In this work, we leverage the sam…
Allowing Safe Contact in Robotic Goal-Reaching: Planning and Tracking in Operational and Null Spaces
Xinghao Zhu, Wenzhao Lian, Bodi Yuan +2
In recent years, impressive results have been achieved in robotic manipulation. While many efforts focus on generating collision-free reference signals, few allow safe contact betw…
Practical tradeoffs between memory, compute, and performance in learned optimizers
Luke Metz, C. Daniel Freeman, James Harrison +2
Optimization plays a costly and crucial role in developing machine learning systems. In learned optimizers, the few hyperparameters of commonly used hand-designed optimizers, e.g.…
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…
Brax -- A Differentiable Physics Engine for Large Scale Rigid Body Simulation
C. Daniel Freeman, Erik Frey, Anton Raichuk +3
We present Brax, an open source library for rigid body simulation with a focus on performance and parallelism on accelerators, written in JAX. We present results on a suite of task…