265 citations · 310 across the 5 of their papers we have counts for
7 papers
Evaluating the Apperception Engine
Richard Evans, Jose Hernandez-Orallo, Johannes Welbl +2
The Apperception Engine is an unsupervised learning system. Given a sequence of sensory inputs, it constructs a symbolic causal theory that both explains the sensory sequence and a…
Making sense of sensory input
Richard Evans, Jose Hernandez-Orallo, Johannes Welbl +2
This paper attempts to answer a central question in unsupervised learning: what does it mean to "make sense" of a sensory sequence? In our formalization, making sense involves cons…
Can Neural Networks Understand Logical Entailment?
Richard Evans, David Saxton, David Amos +2
We introduce a new dataset of logical entailments for the purpose of measuring models' ability to capture and exploit the structure of logical expressions against an entailment pre…
Learning Explanatory Rules from Noisy Data
Richard Evans, Edward Grefenstette
Artificial Neural Networks are powerful function approximators capable of modelling solutions to a wide variety of problems, both supervised and unsupervised. As their size and exp…
Deep Reinforcement Learning with Attention for Slate Markov Decision Processes with High-Dimensional States and Actions
Peter Sunehag, Richard Evans, Gabriel Dulac-Arnold +3
Many real-world problems come with action spaces represented as feature vectors. Although high-dimensional control is a largely unsolved problem, there has recently been progress f…
Deep Reinforcement Learning in Large Discrete Action Spaces
Gabriel Dulac-Arnold, Richard Evans, Hado van Hasselt +7
Being able to reason in an environment with a large number of discrete actions is essential to bringing reinforcement learning to a larger class of problems. Recommender systems, i…
Reinforcement Learning in a Neurally Controlled Robot Using Dopamine Modulated STDP
Richard Evans
Recent work has shown that dopamine-modulated STDP can solve many of the issues associated with reinforcement learning, such as the distal reward problem. Spiking neural networks p…