271 citations · 1k across the 10 of their papers we have counts for
10 papers
Modeling Human Eye Movements with Neural Networks in a Maze-Solving Task
Jason Li, Nicholas Watters, Yingting +3
From smoothly pursuing moving objects to rapidly shifting gazes during visual search, humans employ a wide variety of eye movement strategies in different contexts. While eye movem…
Modular Object-Oriented Games: A Task Framework for Reinforcement Learning, Psychology, and Neuroscience
Nicholas Watters, Joshua Tenenbaum, Mehrdad Jazayeri
In recent years, trends towards studying simulated games have gained momentum in the fields of artificial intelligence, cognitive science, psychology, and neuroscience. The interse…
Unsupervised Model Selection for Variational Disentangled Representation Learning
Sunny Duan, Loic Matthey, Andre Saraiva +4
Disentangled representations have recently been shown to improve fairness, data efficiency and generalisation in simple supervised and reinforcement learning tasks. To extend the b…
COBRA: Data-Efficient Model-Based RL through Unsupervised Object Discovery and Curiosity-Driven Exploration
Nicholas Watters, Loic Matthey, Matko Bosnjak +2
Data efficiency and robustness to task-irrelevant perturbations are long-standing challenges for deep reinforcement learning algorithms. Here we introduce a modular approach to add…
Multi-Object Representation Learning with Iterative Variational Inference
Klaus Greff, Raphaël Lopez Kaufman, Rishabh Kabra +6
Human perception is structured around objects which form the basis for our higher-level cognition and impressive systematic generalization abilities. Yet most work on representatio…
MONet: Unsupervised Scene Decomposition and Representation
Christopher P. Burgess, Loic Matthey, Nicholas Watters +4
The ability to decompose scenes in terms of abstract building blocks is crucial for general intelligence. Where those basic building blocks share meaningful properties, interaction…