35 citations · 67 across the 9 of their papers we have counts for
4 papers · 1 filter
Assessing SATNet's Ability to Solve the Symbol Grounding Problem
Oscar Chang, Lampros Flokas, Hod Lipson +1
SATNet is an award-winning MAXSAT solver that can be used to infer logical rules and integrated as a differentiable layer in a deep neural network. It had been shown to solve Sudok…
Balanced and Deterministic Weight-sharing Helps Network Performance
Oscar Chang, Hod Lipson
Weight-sharing plays a significant role in the success of many deep neural networks, by increasing memory efficiency and incorporating useful inductive priors about the problem int…
Principled Weight Initialization for Hypernetworks
Oscar Chang, Lampros Flokas, Hod Lipson
Hypernetworks are meta neural networks that generate weights for a main neural network in an end-to-end differentiable manner. Despite extensive applications ranging from multi-tas…
Accelerating Meta-Learning by Sharing Gradients
Oscar Chang, Hod Lipson
The success of gradient-based meta-learning is primarily attributed to its ability to leverage related tasks to learn task-invariant information. However, the absence of interactio…