Naren Ramakrishnan

research

∙ 04/06/2022

Improving Zero-Shot Event Extraction via Sentence Simplification

The success of sites such as ACLED and Our World in Data have demonstrat...

Sneha Mehta, et al. ∙

research

∙ 03/04/2022

Contrastive Graph Convolutional Networks for Hardware Trojan Detection in Third Party IP Cores

The availability of wide-ranging third-party intellectual property (3PIP...

Nikhil Muralidhar, et al. ∙

research

∙ 02/21/2022

EINNs: Epidemiologically-Informed Neural Networks

We introduce a new class of physics-informed neural networks-EINN-crafte...

Alexander Rodriguez, et al. ∙

research

∙ 11/09/2021

Deep diffusion-based forecasting of COVID-19 by incorporating network-level mobility information

Modeling the spatiotemporal nature of the spread of infectious diseases ...

Padmaksha Roy, et al. ∙

research

∙ 10/11/2021

TCube: Domain-Agnostic Neural Time-series Narration

The task of generating rich and fluent narratives that aptly describe th...

Mandar Sharma, et al. ∙

research

∙ 06/30/2021

Using AntiPatterns to avoid MLOps Mistakes

We describe lessons learned from developing and deploying machine learni...

Nikhil Muralidhar, et al. ∙

research

∙ 04/02/2021

Detecting Anomalies Through Contrast in Heterogeneous Data

Detecting anomalies has been a fundamental approach in detecting potenti...

Debanjan Datta, et al. ∙

research

∙ 12/11/2020

Better call Surrogates: A hybrid Evolutionary Algorithm for Hyperparameter optimization

In this paper, we propose a surrogate-assisted evolutionary algorithm (E...

Subhodip Biswas, et al. ∙

research

∙ 09/27/2020

STAN: Synthetic Network Traffic Generation using Autoregressive Neural Models

Deep learning models have achieved great success in recent years. Howeve...

Shengzhe Xu, et al. ∙

research

∙ 09/23/2020

Steering a Historical Disease Forecasting Model Under a Pandemic: Case of Flu and COVID-19

Forecasting influenza in a timely manner aids health organizations and p...

Alexander Rodriguez, et al. ∙

research

∙ 09/06/2020

Once Upon A Time In Visualization: Understanding the Use of Textual Narratives for Causality

Causality visualization can help people understand temporal chains of ev...

Arjun Choudhry, et al. ∙

research

∙ 11/26/2019

Low Rank Factorization for Compact Multi-Head Self-Attention

Effective representation learning from text has been an active area of r...

Sneha Mehta, et al. ∙

research

∙ 11/06/2019

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...

Nikhil Muralidhar, et al. ∙

research

∙ 07/17/2019

Mitigating Uncertainty in Document Classification

The uncertainty measurement of classifiers' predictions is especially im...

Xuchao Zhang, et al. ∙

research

∙ 05/22/2019

Patent Citation Dynamics Modeling via Multi-Attention Recurrent Networks

Modeling and forecasting forward citations to a patent is a central task...

Taoran Ji, et al. ∙

research

∙ 12/05/2018

Neural Abstractive Text Summarization with Sequence-to-Sequence Models

In the past few years, neural abstractive text summarization with sequen...

Tian Shi, et al. ∙

research

∙ 10/15/2018

Deep Transfer Reinforcement Learning for Text Summarization

Deep neural networks are data hungry models and thus they face difficult...

Yaser Keneshloo, et al. ∙

research

∙ 05/24/2018

Deep Reinforcement Learning For Sequence to Sequence Models

In recent years, sequence-to-sequence (seq2seq) models are used in a var...

Yaser Keneshloo, et al. ∙

research

∙ 02/22/2017

Distributed Representation of Subgraphs

Network embeddings have become very popular in learning effective featur...

Bijaya Adhikari, et al. ∙

research

∙ 02/22/2017

SIGNet: Scalable Embeddings for Signed Networks

Recent successes in word embedding and document embedding have motivated...

Mohammad Raihanul Islam, et al. ∙

research

∙ 02/22/2017

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

Real-time monitoring and responses to emerging public health threats rel...

Saurav Ghosh, et al. ∙

research

∙ 06/01/2016

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

In retrospective assessments, internet news reports have been shown to c...

Saurav Ghosh, et al. ∙

research

∙ 03/31/2016

Hierarchical Quickest Change Detection via Surrogates

Change detection (CD) in time series data is a critical problem as it re...

Prithwish Chakraborty, et al. ∙

research

∙ 03/01/2016

Characterizing Diseases from Unstructured Text: A Vocabulary Driven Word2vec Approach

Traditional disease surveillance can be augmented with a wide variety of...

Saurav Ghosh, et al. ∙

research

∙ 02/21/2016

Interactive Storytelling over Document Collections

Storytelling algorithms aim to 'connect the dots' between disparate docu...

Dipayan Maiti, et al. ∙

research

∙ 02/17/2010

Efficiently Discovering Hammock Paths from Induced Similarity Networks

Similarity networks are important abstractions in many information manag...

M. Shahriar Hossain, et al. ∙

research

∙ 06/22/2009

Recommender Systems for the Conference Paper Assignment Problem

Conference paper assignment, i.e., the task of assigning paper submissio...

Don Conry, et al. ∙

research

∙ 07/21/2002

Reinforcing Reachable Routes

This paper studies the evaluation of routing algorithms from the perspec...

Srinidhi Varadarajan, et al. ∙

research

∙ 05/14/2002

The Traits of the Personable

Information personalization is fertile ground for application of AI tech...

Naren Ramakrishnan, et al. ∙

research

∙ 04/26/2002

Qualitative Analysis of Correspondence for Experimental Algorithmics

Correspondence identifies relationships among objects via similarities a...

Chris Bailey-Kellogg, et al. ∙

research

∙ 04/22/2002

Sampling Strategies for Mining in Data-Scarce Domains

Data mining has traditionally focused on the task of drawing inferences ...

Naren Ramakrishnan, et al. ∙

research

∙ 10/18/1999

PIPE: Personalizing Recommendations via Partial Evaluation

It is shown that personalization of web content can be advantageously vi...

Naren Ramakrishnan, et al. ∙

Naren Ramakrishnan

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