747 citations · 2k across the 30 of their papers we have counts for
11 papers · 1 filter
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…
Neural-Symbolic VQA: Disentangling Reasoning from Vision and Language Understanding
Kexin Yi, Jiajun Wu, Chuang Gan +3
We marry two powerful ideas: deep representation learning for visual recognition and language understanding, and symbolic program execution for reasoning. Our neural-symbolic visua…
Learning to Understand Goal Specifications by Modelling Reward
Dzmitry Bahdanau, Felix Hill, Jan Leike +4
Recent work has shown that deep reinforcement-learning agents can learn to follow language-like instructions from infrequent environment rewards. However, this places on environmen…
Value Propagation Networks
Nantas Nardelli, Gabriel Synnaeve, Zeming Lin +3
We present Value Propagation (VProp), a set of parameter-efficient differentiable planning modules built on Value Iteration which can successfully be trained using reinforcement le…
Semantic Code Repair using Neuro-Symbolic Transformation Networks
Jacob Devlin, Jonathan Uesato, Rishabh Singh +1
We study the problem of semantic code repair, which can be broadly defined as automatically fixing non-syntactic bugs in source code. The majority of past work in semantic code rep…