1k citations · 1.1k across the 3 of their papers we have counts for
3 papers
Q-Learning in enormous action spaces via amortized approximate maximization
Tom Van de Wiele, David Warde-Farley, Andriy Mnih +1
Applying Q-learning to high-dimensional or continuous action spaces can be difficult due to the required maximization over the set of possible actions. Motivated by techniques from…
Attentive Neural Processes
Hyunjik Kim, Andriy Mnih, Jonathan Schwarz +5
Neural Processes (NPs) (Garnelo et al 2018a;b) approach regression by learning to map a context set of observed input-output pairs to a distribution over regression functions. Each…
The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables
Chris J. Maddison, Andriy Mnih, Yee Whye Teh
The reparameterization trick enables optimizing large scale stochastic computation graphs via gradient descent. The essence of the trick is to refactor each stochastic node into a…