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Computer Science

D-Index
65
Citations
19241
World Ranking
2435
National Ranking
1219

Overview

Sanjiv Kumar is a researcher affiliated with Google in the United States, contributing primarily to the fields of computer science and medicine. Their research portfolio encompasses a wide range of topics, focusing on artificial intelligence, computer vision and pattern recognition, as well as medical subfields such as surgery, general health professions, and pulmonary and respiratory medicine.

The main areas of study for Sanjiv Kumar include:

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Surgery
  • General Health Professions
  • Pulmonary and Respiratory Medicine

Their work covers various core topics in machine learning and data science:

  • Topic Modeling
  • Natural Language Processing Techniques
  • Domain Adaptation and Few-Shot Learning
  • Multimodal Machine Learning Applications
  • Machine Learning and Data Classification
  • Advanced Neural Network Applications
  • Advanced Image and Video Retrieval Techniques

Sanjiv Kumar has co-authored numerous scientific papers with frequent collaborators including Ankit Singh Rawat, Sashank J. Reddi, Aditya Krishna Menon, Srinadh Bhojanapalli, and Manzil Zaheer.

Their recent publications highlight a focus on federated learning, transformer models, and large-scale retrieval techniques. Selected recent works include:

  • Adaptive Federated Optimization, 2020, arXiv (Cornell University)
  • Pre-training Tasks for Embedding-based Large-scale Retrieval, 2020, arXiv (Cornell University)
  • Modifying Memories in Transformer Models, 2020, arXiv (Cornell University)
  • Federated Learning with Only Positive Labels, 2020, arXiv (Cornell University)
  • $O(n)$ Connections are Expressive Enough: Universal Approximability of Sparse Transformers, 2020, arXiv (Cornell University)

The majority of Sanjiv Kumar's publications have appeared in arXiv (Cornell University), with additional contributions found in venues such as Cureus, the Journal of Drug Research in Ayurvedic Sciences, Journal of Bodywork and Movement Therapies, and the Journal of Clinical and Diagnostic Research.

Best Publications

  • On the Convergence of Adam and Beyond

    Sashank J. Reddi;Satyen Kale;Sanjiv Kumar

  • Hashing with Graphs

    Wei Liu;Jun Wang;Sanjiv Kumar;Shih-fu Chang

  • Semi-Supervised Hashing for Large-Scale Search

    Jun Wang;S. Kumar;Shih-Fu Chang

  • Semi-supervised hashing for scalable image retrieval

    Jun Wang;Sanjiv Kumar;Shih-Fu Chang

  • Discriminative random fields: a discriminative framework for contextual interaction in classification

    Sanjiv Kumar;Hebert

  • A New Baseline for Image Annotation

    Ameesh Makadia;Vladimir Pavlovic;Sanjiv Kumar

  • Face tracking and recognition with visual constraints in real-world videos

    Minyoung Kim;S. Kumar;V. Pavlovic;H. Rowley

  • Learning to Hash for Indexing Big Data—A Survey

    Jun Wang;Wei Liu;Sanjiv Kumar;Shih-Fu Chang

  • Discrete Graph Hashing

    Wei Liu;Cun Mu;Sanjiv Kumar;Shih-Fu Chang

  • Discriminative Random Fields

    Sanjiv Kumar;Martial Hebert

  • Large Batch Optimization for Deep Learning: Training BERT in 76 minutes

    Yang You;Jing Li;Sashank Reddi;Jonathan Hseu

  • Sequential Projection Learning for Hashing with Compact Codes

    Jun Wang;Sanjiv Kumar;Shih-fu Chang

  • Sampling methods for the Nyström method

    Sanjiv Kumar;Mehryar Mohri;Ameet Talwalkar

  • A hierarchical field framework for unified context-based classification

    S. Kumar;M. Hebert

  • An Exploration of Parameter Redundancy in Deep Networks with Circulant Projections

    Yu Cheng;Yu Cheng;Felix X. Yu;Rogerio S. Feris;Sanjiv Kumar

  • cpSGD: communication-efficient and differentially-private distributed SGD

    Naman Agarwal;Ananda Theertha Suresh;Felix Yu;Sanjiv Kumar

  • Long-tail learning via logit adjustment

    Aditya Krishna Menon;Sadeep Jayasumana;Ankit Singh Rawat;Himanshu Jain

  • Discriminative Fields for Modeling Spatial Dependencies in Natural Images

    Sanjiv Kumar;Martial Hebert

  • Baselines for Image Annotation

    Ameesh Makadia;Vladimir Pavlovic;Sanjiv Kumar

  • Large-scale manifold learning

    A. Talwalkar;S. Kumar;H. Rowley

  • Adaptive Methods for Nonconvex Optimization

    Manzil Zaheer;Sashank J. Reddi;Devendra Singh Sachan;Satyen Kale

  • Efficient Natural Language Response Suggestion for Smart Reply

    Matthew L. Henderson;Rami Al-Rfou;Brian Strope;Yun-Hsuan Sung

  • Adaptive Federated Optimization

    Sashank Reddi;Zachary Charles;Manzil Zaheer;Zachary Garrett

  • Accelerating Large-Scale Inference with Anisotropic Vector Quantization

    Ruiqi Guo;Quan Geng;David Simcha;Felix Chern

Frequent Co-Authors

Felix X. Yu
Felix X. Yu Google (United States)
Sashank J. Reddi
Sashank J. Reddi Google (United States)
Henry Allan Rowley
Henry Allan Rowley Google (United States)
Shih-Fu Chang
Shih-Fu Chang Columbia University
Cho-Jui Hsieh
Cho-Jui Hsieh University of California, Los Angeles
Satyen Kale
Satyen Kale Google (United States)
Martial Hebert
Martial Hebert Carnegie Mellon University
Tony Jebara
Tony Jebara Columbia University
Ameet Talwalkar
Ameet Talwalkar Carnegie Mellon University

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