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Varun Mithal

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3 papers

1 author row

TIST Journal 2021 Journal Article

An Attentive Survey of Attention Models

Sneha Chaudhari
Varun Mithal
Gungor Polatkan
Rohan Ramanath

Attention Model has now become an important concept in neural networks that has been researched within diverse application domains. This survey provides a structured and comprehensive overview of the developments in modeling attention. In particular, we propose a taxonomy that groups existing techniques into coherent categories. We review salient neural architectures in which attention has been incorporated and discuss applications in which modeling attention has shown a significant impact. We also describe how attention has been used to improve the interpretability of neural networks. Finally, we discuss some future research directions in attention. We hope this survey will provide a succinct introduction to attention models and guide practitioners while developing approaches for their applications.

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AAAI Conference 2013 Conference Paper

Multiple Hypothesis Object Tracking For Unsupervised Self-Learning: An Ocean Eddy Tracking Application

James Faghmous
Muhammed Uluyol
Luke Styles
Matthew Le
Varun Mithal
Shyam Boriah
Vipin Kumar

Mesoscale ocean eddies transport heat, salt, energy, and nutrients across oceans. As a result, accurately identifying and tracking such phenomena are crucial for understanding ocean dynamics and marine ecosystem sustainability. Traditionally, ocean eddies are monitored through two phases: identiﬁcation and tracking. A major challenge for such an approach is that the tracking phase is dependent on the performance of the identiﬁcation scheme, which can be susceptible to noise and sampling errors. In this paper, we focus on tracking, and introduce the concept of multiple hypothesis assignment (MHA), which extends traditional multiple hypothesis tracking for cases where the features tracked are noisy or uncertain. Under this scheme, features are assigned to multiple potential tracks, and the ﬁnal assignment is deferred until more data are available to make a relatively unambiguous decision. Unlike the most widely used methods in the eddy tracking literature, MHA uses contextual spatio-temporal information to take corrective measures autonomously on the detection step a posteriori and performs signiﬁcantly better in the presence of noise. This study is also the ﬁrst to empirically analyze the relative robustness of eddy tracking algorithms.

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TIST Journal 2011 Journal Article

Monitoring global forest cover using data mining

Varun Mithal
Ashish Garg
Shyam Boriah
Michael Steinbach
Vipin Kumar
Christopher Potter
Steven Klooster
Juan Carlos Castilla-Rubio

Forests are a critical component of the planet's ecosystem. Unfortunately, there has been significant degradation in forest cover over recent decades as a result of logging, conversion to crop, plantation, and pasture land, or disasters (natural or man made) such as forest fires, floods, and hurricanes. As a result, significant attention is being given to the sustainable use of forests. A key to effective forest management is quantifiable knowledge about changes in forest cover. This requires identification and characterization of changes and the discovery of the relationship between these changes and natural and anthropogenic variables. In this article, we present our preliminary efforts and achievements in addressing some of these tasks along with the challenges and opportunities that need to be addressed in the future. At a higher level, our goal is to provide an overview of the exciting opportunities and challenges in developing and applying data mining approaches to provide critical information for forest and land use management.

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