1 citations · 1 across the 3 of their papers we have counts for
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On the geometry and topology of representations: the manifolds of modular addition
Gabriela Moisescu-Pareja, Gavin McCracken, Harley Wiltzer +4
The Clock and Pizza interpretations, associated with architectures differing in either uniform or learnable attention, were introduced to argue that different architectural designs…
Uncovering a Universal Abstract Algorithm for Modular Addition in Neural Networks
Gavin McCracken, Gabriela Moisescu-Pareja, Vincent Letourneau +2
We propose a testable universality hypothesis, asserting that seemingly disparate neural network solutions observed in the simple task of modular addition are unified under a commo…
A Study of Policy Gradient on a Class of Exactly Solvable Models
Gavin McCracken, Colin Daniels, Rosie Zhao +3
Policy gradient methods are extensively used in reinforcement learning as a way to optimize expected return. In this paper, we explore the evolution of the policy parameters, for a…
oIRL: Robust Adversarial Inverse Reinforcement Learning with Temporally Extended Actions
David Venuto, Jhelum Chakravorty, Leonard Boussioux +3
Explicit engineering of reward functions for given environments has been a major hindrance to reinforcement learning methods. While Inverse Reinforcement Learning (IRL) is a soluti…