107 citations · 1k across the 45 of their papers we have counts for
7 papers · 1 filter
Synthetic Datasets for Neural Program Synthesis
Richard Shin, Neel Kant, Kavi Gupta +4
The goal of program synthesis is to automatically generate programs in a particular language from corresponding specifications, e.g. input-output behavior. Many current approaches…
Learning Transferable Graph Exploration
Hanjun Dai, Yujia Li, Chenglong Wang +3
This paper considers the problem of efficient exploration of unseen environments, a key challenge in AI. We propose a `learning to explore' framework where we learn a policy from a…
MoËT: Mixture of Expert Trees and its Application to Verifiable Reinforcement Learning
Marko Vasic, Andrija Petrovic, Kaiyuan Wang +3
Rapid advancements in deep learning have led to many recent breakthroughs. While deep learning models achieve superior performance, often statistically better than humans, their ad…
A New Uncertainty Framework for Stochastic Signal Processing
Rishabh Singh, Jose C. Principe
The fields of signal processing and information theory have evolved with the goal of developing formulations to extract intrinsic information from limited amount of data. When one…
SyGuS-Comp 2018: Results and Analysis
Rajeev Alur, Dana Fisman, Saswat Padhi +2
Syntax-guided synthesis (SyGuS) is the computational problem of finding an implementation that meets both a semantic constraint given by a logical formula in a background t…
Neural Program Repair by Jointly Learning to Localize and Repair
Marko Vasic, Aditya Kanade, Petros Maniatis +2
Due to its potential to improve programmer productivity and software quality, automated program repair has been an active topic of research. Newer techniques harness neural network…