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20152026
most citedHierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation

822 citations · 2.9k across the 43 of their papers we have counts for

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Showing 2016Show all

5 papers · 1 filter

cs.CL2016★ 1 cited

Neural Generation of Regular Expressions from Natural Language with Minimal Domain Knowledge

Nicholas Locascio, Karthik Narasimhan, Eduardo DeLeon +2

This paper explores the task of translating natural language queries into regular expressions which embody their meaning. In contrast to prior work, the proposed neural model does…

cs.PL2016★ 4 cited

sk_p: a neural program corrector for MOOCs

Yewen Pu, Karthik Narasimhan, Armando Solar-Lezama +1

We present a novel technique for automatic program correction in MOOCs, capable of fixing both syntactic and semantic errors without manual, problem specific correction strategies.…

cs.LG2016★ 822 cited

Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation

Tejas D. Kulkarni, Karthik R. Narasimhan, Ardavan Saeedi +1

Learning goal-directed behavior in environments with sparse feedback is a major challenge for reinforcement learning algorithms. The primary difficulty arises due to insufficient e…

cs.CL2016★ 8 cited

Nonparametric Spherical Topic Modeling with Word Embeddings

Kayhan Batmanghelich, Ardavan Saeedi, Karthik Narasimhan +1

Traditional topic models do not account for semantic regularities in language. Recent distributional representations of words exhibit semantic consistency over directional metrics…

cs.CL2016★ 34 cited

Improving Information Extraction by Acquiring External Evidence with Reinforcement Learning

Karthik Narasimhan, Adam Yala, Regina Barzilay

Most successful information extraction systems operate with access to a large collection of documents. In this work, we explore the task of acquiring and incorporating external evi…