Karthik Narasimhan

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MIT ∙ Princeton University

Calibration, Entropy Rates, and Memory in Language Models

Building accurate language models that capture meaningful long-term depe...
06/11/2019 ∙ by Mark Braverman, et al. ∙ 1 ∙ share

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sk_p: a neural program corrector for MOOCs

We present a novel technique for automatic program correction in MOOCs, ...
07/11/2016 ∙ by Yewen Pu, et al. ∙ 0 ∙ share

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JUMP-Means: Small-Variance Asymptotics for Markov Jump Processes

Markov jump processes (MJPs) are used to model a wide range of phenomena...
03/01/2015 ∙ by Jonathan H. Huggins, et al. ∙ 0 ∙ share

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Deep Transfer in Reinforcement Learning by Language Grounding

In this paper, we explore the utilization of natural language to drive t...
08/01/2017 ∙ by Karthik Narasimhan, et al. ∙ 0 ∙ share

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Representation Learning for Grounded Spatial Reasoning

The interpretation of spatial references is highly contextual, requiring...
07/13/2017 ∙ by Michael Janner, et al. ∙ 0 ∙ share

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Unsupervised Learning of Morphological Forests

This paper focuses on unsupervised modeling of morphological families, c...
02/22/2017 ∙ by Jiaming Luo, et al. ∙ 0 ∙ share

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

This paper explores the task of translating natural language queries int...
08/09/2016 ∙ by Nicholas Locascio, et al. ∙ 0 ∙ share

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Nonparametric Spherical Topic Modeling with Word Embeddings

Traditional topic models do not account for semantic regularities in lan...
04/01/2016 ∙ by Kayhan Batmanghelich, et al. ∙ 0 ∙ share

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Improving Information Extraction by Acquiring External Evidence with Reinforcement Learning

Most successful information extraction systems operate with access to a ...
03/25/2016 ∙ by Karthik Narasimhan, et al. ∙ 0 ∙ share

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Language Understanding for Text-based Games Using Deep Reinforcement Learning

In this paper, we consider the task of learning control policies for tex...
06/30/2015 ∙ by Karthik Narasimhan, et al. ∙ 0 ∙ share

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An Unsupervised Method for Uncovering Morphological Chains

Most state-of-the-art systems today produce morphological analysis based...
03/08/2015 ∙ by Karthik Narasimhan, et al. ∙ 0 ∙ share

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Task-Agnostic Dynamics Priors for Deep Reinforcement Learning

While model-based deep reinforcement learning (RL) holds great promise f...
05/13/2019 ∙ by Yilun Du, et al. ∙ 0 ∙ share

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A Generalized Algorithm for Multi-Objective Reinforcement Learning and Policy Adaptation

We introduce a new algorithm for multi-objective reinforcement learning ...
08/21/2019 ∙ by Runzhe Yang, et al. ∙ 0 ∙ share

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Karthik Narasimhan

Featured Co-authors

Yi Zhang

Sham M. Kakade

Regina Barzilay

Tommi Jaakkola

Armando Solar-Lezama

Jonathan H. Huggins

Vikash K. Mansinghka

Kayhan Batmanghelich

Mark Braverman

Tejas Kulkarni

Ardavan Saeedi

Calibration, Entropy Rates, and Memory in Language Models

sk_p: a neural program corrector for MOOCs

JUMP-Means: Small-Variance Asymptotics for Markov Jump Processes

Deep Transfer in Reinforcement Learning by Language Grounding

Representation Learning for Grounded Spatial Reasoning

Unsupervised Learning of Morphological Forests

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

Nonparametric Spherical Topic Modeling with Word Embeddings

Improving Information Extraction by Acquiring External Evidence with Reinforcement Learning

Language Understanding for Text-based Games Using Deep Reinforcement Learning

An Unsupervised Method for Uncovering Morphological Chains

Task-Agnostic Dynamics Priors for Deep Reinforcement Learning

A Generalized Algorithm for Multi-Objective Reinforcement Learning and Policy Adaptation