10.1k citations · 22.1k across the 78 of their papers we have counts for
6 papers · 1 filter
An Efficient Algorithm for Thresholding Monte Carlo Tree Search
Shoma Nameki, Atsuyoshi Nakamura, Junpei Komiyama +1
We introduce the Thresholding Monte Carlo Tree Search problem, in which, given a tree and a threshold , a player must answer whether the root node value of $\mathc…
Training Generative Adversarial Networks by Solving Ordinary Differential Equations
Chongli Qin, Yan Wu, Jost Tobias Springenberg +4
The instability of Generative Adversarial Network (GAN) training has frequently been attributed to gradient descent. Consequently, recent methods have aimed to tailor the models an…
Meta-Learning Deep Energy-Based Memory Models
Sergey Bartunov, Jack W Rae, Simon Osindero +1
We study the problem of learning associative memory -- a system which is able to retrieve a remembered pattern based on its distorted or incomplete version. Attractor networks prov…
Noise Contrastive Priors for Functional Uncertainty
Danijar Hafner, Dustin Tran, Timothy Lillicrap +2
Obtaining reliable uncertainty estimates of neural network predictions is a long standing challenge. Bayesian neural networks have been proposed as a solution, but it remains open…
The Kanerva Machine: A Generative Distributed Memory
Yan Wu, Greg Wayne, Alex Graves +1
We present an end-to-end trained memory system that quickly adapts to new data and generates samples like them. Inspired by Kanerva's sparse distributed memory, it has a robust dis…
Learning to Learn without Gradient Descent by Gradient Descent
Yutian Chen, Matthew W. Hoffman, Sergio Gomez Colmenarejo +4
We learn recurrent neural network optimizers trained on simple synthetic functions by gradient descent. We show that these learned optimizers exhibit a remarkable degree of transfe…