2 citations · 3 across the 2 of their papers we have counts for
Showing cs.NEShow all
3 papers · 1 filter
cs.NE2018
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…
cs.NE2017
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…
cs.NE2015★ 2 cited
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…