747 citations · 3.3k across the 61 of their papers we have counts for
7 papers · 2 filters
A Unified View of Piecewise Linear Neural Network Verification
Rudy Bunel, Ilker Turkaslan, Philip H. S. Torr +2
The success of Deep Learning and its potential use in many safety-critical applications has motivated research on formal verification of Neural Network (NN) models. Despite the rep…
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…
Neural Program Meta-Induction
Jacob Devlin, Rudy Bunel, Rishabh Singh +2
Most recently proposed methods for Neural Program Induction work under the assumption of having a large set of input/output (I/O) examples for learning any underlying input-output…
Zero-Shot Task Generalization with Multi-Task Deep Reinforcement Learning
Junhyuk Oh, Satinder Singh, Honglak Lee +1
As a step towards developing zero-shot task generalization capabilities in reinforcement learning (RL), we introduce a new RL problem where the agent should learn to execute sequen…
Deep API Programmer: Learning to Program with APIs
Surya Bhupatiraju, Rishabh Singh, Abdel-rahman Mohamed +1
We present DAPIP, a Programming-By-Example system that learns to program with APIs to perform data transformation tasks. We design a domain-specific language (DSL) that allows for…
RobustFill: Neural Program Learning under Noisy I/O
Jacob Devlin, Jonathan Uesato, Surya Bhupatiraju +3
The problem of automatically generating a computer program from some specification has been studied since the early days of AI. Recently, two competing approaches for automatic pro…