Gradients are Not All You Need (Machine Learning Research Paper Explained)

#deeplearning #backpropagation #simulation

More and more systems are made differentiable, which means that accurate gradients of these systems' dynamics can be computed exactly. While this development has led to a lot of advances, there are also distinct situations where backpropagation can be a very bad idea. This paper characterizes a few such systems in the domain of iterated dynamical systems, often including some source of stochasticity, resulting in chaotic behavior. In these systems, it is often better to use black-box estimators for gradients than computing them exactly.

OUTLINE:

0:00 - Foreword

1:15 - Intro & Overview

3:40 - Backpropagation through iterated systems

12:10 - Connection to the spectrum of the Jacobian

15:35 - The Reparameterization Trick

21:30 - Problems of reparameterization

26:35 - Example 1: Policy Learning in Simulation

33:05 - Example 2: Meta-Learning Optimizers

36:15 - Example 3: Disk packing

37:45 - Analysis of Jacobians

40:20 - What can be done?

45:40 - Just use Black-Box methods

Paper: https://arxiv.org/abs/2111.05803

Abstract:

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 powerful, they have limits. In this short report, we discuss a common chaos based failure mode which appears in a variety of differentiable circumstances, ranging from recurrent neural networks and numerical physics simulation to training learned optimizers. We trace this failure to the spectrum of the Jacobian of the system under study, and provide criteria for when a practitioner might expect this failure to spoil their differentiation based optimization algorithms.

Authors: Luke Metz, C. Daniel Freeman, Samuel S. Schoenholz, Tal Kachman

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