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5 papers · 2 filters
BBQ-Networks: Efficient Exploration in Deep Reinforcement Learning for Task-Oriented Dialogue Systems
Zachary Lipton, Xiujun Li, Jianfeng Gao +3
We present a new algorithm that significantly improves the efficiency of exploration for deep Q-learning agents in dialogue systems. Our agents explore via Thompson sampling, drawi…
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…
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…
Learn&Fuzz: Machine Learning for Input Fuzzing
Patrice Godefroid, Hila Peleg, Rishabh Singh
Fuzzing consists of repeatedly testing an application with modified, or fuzzed, inputs with the goal of finding security vulnerabilities in input-parsing code. In this paper, we sh…
Space-Time Graph Modeling of Ride Requests Based on Real-World Data
Abhinav Jauhri, Brian Foo, Jerome Berclaz +4
This paper focuses on modeling ride requests and their variations over location and time, based on analyzing extensive real-world data from a ride-sharing service. We introduce a g…