Naren Ramakrishnan

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Virginia Polytechnic Institute and State University

Thomas L. Phillips Professor of Engineering and Director, Discovery Analytics Center at Virginia Tech

Physics-guided Design and Learning of Neural Networks for Predicting Drag Force on Particle Suspensions in Moving Fluids

Physics-based simulations are often used to model and understand complex...
11/06/2019 ∙ by Nikhil Muralidhar, et al. ∙ 22 ∙ share

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Neural Abstractive Text Summarization with Sequence-to-Sequence Models

In the past few years, neural abstractive text summarization with sequen...
12/05/2018 ∙ by Tian Shi, et al. ∙ 8 ∙ share

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Deep Transfer Reinforcement Learning for Text Summarization

Deep neural networks are data hungry models and thus they face difficult...
10/15/2018 ∙ by Yaser Keneshloo, et al. ∙ 6 ∙ share

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Patent Citation Dynamics Modeling via Multi-Attention Recurrent Networks

Modeling and forecasting forward citations to a patent is a central task...
05/22/2019 ∙ by Taoran Ji, et al. ∙ 3 ∙ share

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Mitigating Uncertainty in Document Classification

The uncertainty measurement of classifiers' predictions is especially im...
07/17/2019 ∙ by Xuchao Zhang, et al. ∙ 1 ∙ share

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Distributed Representation of Subgraphs

Network embeddings have become very popular in learning effective featur...
02/22/2017 ∙ by Bijaya Adhikari, et al. ∙ 0 ∙ share

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SIGNet: Scalable Embeddings for Signed Networks

Recent successes in word embedding and document embedding have motivated...
02/22/2017 ∙ by Mohammad Raihanul Islam, et al. ∙ 0 ∙ share

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Hierarchical Quickest Change Detection via Surrogates

Change detection (CD) in time series data is a critical problem as it re...
03/31/2016 ∙ by Prithwish Chakraborty, et al. ∙ 0 ∙ share

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Interactive Storytelling over Document Collections

Storytelling algorithms aim to 'connect the dots' between disparate docu...
02/21/2016 ∙ by Dipayan Maiti, et al. ∙ 0 ∙ share

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Efficiently Discovering Hammock Paths from Induced Similarity Networks

Similarity networks are important abstractions in many information manag...
02/17/2010 ∙ by M. Shahriar Hossain, et al. ∙ 0 ∙ share

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Recommender Systems for the Conference Paper Assignment Problem

Conference paper assignment, i.e., the task of assigning paper submissio...
06/22/2009 ∙ by Don Conry, et al. ∙ 0 ∙ share

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Reinforcing Reachable Routes

This paper studies the evaluation of routing algorithms from the perspec...
07/21/2002 ∙ by Srinidhi Varadarajan, et al. ∙ 0 ∙ share

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The Traits of the Personable

Information personalization is fertile ground for application of AI tech...
05/14/2002 ∙ by Naren Ramakrishnan, et al. ∙ 0 ∙ share

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Qualitative Analysis of Correspondence for Experimental Algorithmics

Correspondence identifies relationships among objects via similarities a...
04/26/2002 ∙ by Chris Bailey-Kellogg, et al. ∙ 0 ∙ share

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Sampling Strategies for Mining in Data-Scarce Domains

Data mining has traditionally focused on the task of drawing inferences ...
04/22/2002 ∙ by Naren Ramakrishnan, et al. ∙ 0 ∙ share

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PIPE: Personalizing Recommendations via Partial Evaluation

It is shown that personalization of web content can be advantageously vi...
10/18/1999 ∙ by Naren Ramakrishnan, et al. ∙ 0 ∙ share

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Guided Deep List: Automating the Generation of Epidemiological Line Lists from Open Sources

Real-time monitoring and responses to emerging public health threats rel...
02/22/2017 ∙ by Saurav Ghosh, et al. ∙ 0 ∙ share

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Temporal Topic Modeling to Assess Associations between News Trends and Infectious Disease Outbreaks

In retrospective assessments, internet news reports have been shown to c...
06/01/2016 ∙ by Saurav Ghosh, et al. ∙ 0 ∙ share

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Characterizing Diseases from Unstructured Text: A Vocabulary Driven Word2vec Approach

Traditional disease surveillance can be augmented with a wide variety of...
03/01/2016 ∙ by Saurav Ghosh, et al. ∙ 0 ∙ share

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Deep Reinforcement Learning For Sequence to Sequence Models

In recent years, sequence-to-sequence (seq2seq) models are used in a var...
05/24/2018 ∙ by Yaser Keneshloo, et al. ∙ 0 ∙ share

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Low Rank Factorization for Compact Multi-Head Self-Attention

Effective representation learning from text has been an active area of r...
11/26/2019 ∙ by Sneha Mehta, et al. ∙ 0 ∙ share

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Naren Ramakrishnan

Featured Co-authors

Yao Zhang

Huzefa Rangwala

Ravi Tandon

Chang-Tien Lu

Anuj Karpatne

Chandan K. Reddy

Xuchao Zhang

Prithwish Chakraborty

Zhiqian Chen

Elaine O. Nsoesie

John S. Brownstein

Physics-guided Design and Learning of Neural Networks for Predicting Drag Force on Particle Suspensions in Moving Fluids

Neural Abstractive Text Summarization with Sequence-to-Sequence Models

Deep Transfer Reinforcement Learning for Text Summarization

Patent Citation Dynamics Modeling via Multi-Attention Recurrent Networks

Mitigating Uncertainty in Document Classification

Distributed Representation of Subgraphs

SIGNet: Scalable Embeddings for Signed Networks

Hierarchical Quickest Change Detection via Surrogates

Interactive Storytelling over Document Collections

Efficiently Discovering Hammock Paths from Induced Similarity Networks

Recommender Systems for the Conference Paper Assignment Problem

Reinforcing Reachable Routes

The Traits of the Personable

Qualitative Analysis of Correspondence for Experimental Algorithmics

Sampling Strategies for Mining in Data-Scarce Domains

PIPE: Personalizing Recommendations via Partial Evaluation

Guided Deep List: Automating the Generation of Epidemiological Line Lists from Open Sources

Temporal Topic Modeling to Assess Associations between News Trends and Infectious Disease Outbreaks

Characterizing Diseases from Unstructured Text: A Vocabulary Driven Word2vec Approach

Deep Reinforcement Learning For Sequence to Sequence Models

Low Rank Factorization for Compact Multi-Head Self-Attention