15 citations · 17 across the 7 of their papers we have counts for
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cs.LG2019
Higher-Order Function Networks for Learning Composable 3D Object Representations
Eric Mitchell, Selim Engin, Volkan Isler +1
We present a new approach to 3D object representation where a neural network encodes the geometry of an object directly into the weights and biases of a second 'mapping' network. T…
cs.LG2019
Reward Prediction Error as an Exploration Objective in Deep RL
Riley Simmons-Edler, Ben Eisner, Daniel Yang +4
A major challenge in reinforcement learning is exploration, when local dithering methods such as epsilon-greedy sampling are insufficient to solve a given task. Many recent methods…