H-Index & Metrics Best Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Engineering and Technology D-index 51 Citations 10,612 151 World Ranking 1409 National Ranking 594

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Statistics
  • Mathematical analysis

Suvrit Sra mainly investigates Mathematical optimization, Artificial intelligence, Variance reduction, Algorithm and Metric. His Mathematical optimization study combines topics from a wide range of disciplines, such as Matrix decomposition, Nonnegative matrix and Riemannian geometry. His study in Artificial intelligence is interdisciplinary in nature, drawing from both Machine learning, Subgradient method, State and Pattern recognition.

His work carried out in the field of Pattern recognition brings together such families of science as Cluster analysis and Expectation–maximization algorithm. The Metric study combines topics in areas such as Positive-definite matrix, Matrix, Manifold, Divergence and Nearest neighbor search. His study on Nearest neighbor search also encompasses disciplines like

  • Function that connect with fields like Kullback–Leibler divergence, Similarity learning, k-nearest neighbors algorithm, Mahalanobis distance and Feature,
  • Covariance matrix that connect with fields like Similarity.

His most cited work include:

  • Information-theoretic metric learning (1617 citations)
  • Clustering on the Unit Hypersphere using von Mises-Fisher Distributions (634 citations)
  • Optimization for Machine Learning (378 citations)

What are the main themes of his work throughout his whole career to date?

Suvrit Sra mostly deals with Mathematical optimization, Applied mathematics, Algorithm, Artificial intelligence and Positive-definite matrix. His study looks at the intersection of Mathematical optimization and topics like Gradient descent with Stationary point. His Applied mathematics study deals with Manifold intersecting with Stochastic optimization.

As part of the same scientific family, Suvrit Sra usually focuses on Algorithm, concentrating on Sampling and intersecting with Markov chain, Probabilistic logic and Distribution. As a member of one scientific family, Suvrit Sra mostly works in the field of Artificial intelligence, focusing on Machine learning and, on occasion, Sample. As a part of the same scientific family, Suvrit Sra mostly works in the field of Positive-definite matrix, focusing on Metric and, on occasion, Representation.

He most often published in these fields:

  • Mathematical optimization (26.14%)
  • Applied mathematics (21.59%)
  • Algorithm (20.83%)

What were the highlights of his more recent work (between 2017-2021)?

  • Applied mathematics (21.59%)
  • Artificial intelligence (20.45%)
  • Mathematical optimization (26.14%)

In recent papers he was focusing on the following fields of study:

Suvrit Sra spends much of his time researching Applied mathematics, Artificial intelligence, Mathematical optimization, Algorithm and Discrete mathematics. His Applied mathematics research is multidisciplinary, incorporating elements of Artificial neural network, Sequence, Gradient method and Convex function. His Artificial intelligence research is multidisciplinary, relying on both Optimization problem, Machine learning and Key.

His biological study spans a wide range of topics, including Manifold, Pooling, Reproducing kernel Hilbert space and Pattern recognition. He interconnects Cover and Computation in the investigation of issues within Mathematical optimization. His Discrete mathematics research incorporates elements of Supervised learning, Upper and lower bounds and Series.

Between 2017 and 2021, his most popular works were:

  • Why Gradient Clipping Accelerates Training: A Theoretical Justification for Adaptivity (41 citations)
  • Small nonlinearities in activation functions create bad local minima in neural networks (36 citations)
  • Small ReLU networks are powerful memorizers: a tight analysis of memorization capacity (35 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Statistics
  • Mathematical analysis

The scientist’s investigation covers issues in Applied mathematics, Artificial intelligence, Deep learning, Stochastic gradient descent and Sequence. The concepts of his Applied mathematics study are interwoven with issues in Artificial neural network, Sigmoid function, Counterexample, Convex function and Gradient method. His Artificial intelligence research includes elements of Machine learning, Key and Pattern recognition.

His work in Deep learning addresses subjects such as Saddle point, which are connected to disciplines such as Computation, Hessian matrix and Mathematical optimization. His work in Stochastic gradient descent addresses issues such as Clipping, which are connected to fields such as Algorithm. He has researched Condition number in several fields, including Manifold, Metric and Convex optimization.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

Information-theoretic metric learning

Jason V. Davis;Brian Kulis;Prateek Jain;Suvrit Sra.
international conference on machine learning (2007)

2225 Citations

Clustering on the Unit Hypersphere using von Mises-Fisher Distributions

Arindam Banerjee;Inderjit S. Dhillon;Joydeep Ghosh;Suvrit Sra.
Journal of Machine Learning Research (2005)

827 Citations

Optimization for Machine Learning

Suvrit Sra;Sebastian Nowozin;Stephen J. Wright.
neural information processing systems (2011)

578 Citations

Generalized Nonnegative Matrix Approximations with Bregman Divergences

Suvrit Sra;Inderjit S. Dhillon.
neural information processing systems (2005)

481 Citations

Minimum sum-squared residue co-clustering of gene expression data

Hyuk Cho;Inderjit S. Dhillon;Yuqiang Guan;Suvrit Sra.
siam international conference on data mining (2004)

400 Citations

Efficient filter flow for space-variant multiframe blind deconvolution

Michael Hirsch;Suvrit Sra;Bernhard Scholkopf;Stefan Harmeling.
computer vision and pattern recognition (2010)

225 Citations

Stochastic Variance Reduction for Nonconvex Optimization

Sashank J. Reddi;Ahmed Hefny;Suvrit Sra;Barnabás Póczós.
arXiv: Optimization and Control (2016)

197 Citations

Randomized Nonlinear Component Analysis

David Lopez-Paz;Suvrit Sra;Alex Smola;Zoubin Ghahramani.
international conference on machine learning (2014)

163 Citations

Jensen-Bregman LogDet Divergence with Application to Efficient Similarity Search for Covariance Matrices

A. Cherian;S. Sra;A. Banerjee;N. Papanikolopoulos.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2013)

160 Citations

Proximal stochastic methods for nonsmooth nonconvex finite-sum optimization

Sashank J. Reddi;Suvrit Sra;Barnabas Poczos;Alexander J. Smola.
neural information processing systems (2016)

156 Citations

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