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Found 8 papers, 4 papers with code
Analysing Mathematical Reasoning Abilities of Neural Models
The structured nature of the mathematics domain, covering arithmetic, algebra, probability and calculus, enables the construction of training and test splits designed to clearly illuminate the capabilities and failure-modes of different architectures, as well as evaluate their ability to compose and relate knowledge and learned processes.
Ranked #2 on
Question Answering
on Mathematics Dataset
Meta-Learning surrogate models for sequential decision making
We introduce a unified probabilistic framework for solving sequential decision making problems ranging from Bayesian optimisation to contextual bandits and reinforcement learning.
Conditional Neural Processes
Deep neural networks excel at function approximation, yet they are typically trained from scratch for each new function.
Unsupervised Predictive Memory in a Goal-Directed Agent
Animals execute goal-directed behaviours despite the limited range and scope of their sensors.
Can Neural Networks Understand Logical Entailment?
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 prediction task.
Learning objects from pixels
We show how discrete objects can be learnt in an unsupervised fashion from pixels, and how to perform reinforcement learning using this object representation.
Programmable Agents
We build deep RL agents that execute declarative programs expressed in formal language.
Unifying Count-Based Exploration and Intrinsic Motivation
We consider an agent's uncertainty about its environment and the problem of generalizing this uncertainty across observations.
Ranked #10 on
Atari Games
on Atari 2600 Montezuma's Revenge
