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Manik Varma

Manik Varma

D-Index & Metrics

Computer Science

D-Index
30
Citations
11743
World Ranking
13817
National Ranking
162

Research.com Recognitions

  • Fellow of the Indian National Academy of Engineering (INAE)
  • Fellow of the Indian National Academy of Engineering (INAE)

Overview

Manik Varma is affiliated with Microsoft (India) and has contributed extensively to the field of computer science, with a primary focus on artificial intelligence and computer vision. Their research spans multiple interconnected areas including machine learning, natural language processing, and smart agriculture.

Their main fields of study include:

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Molecular Biology
  • Plant Science
  • Materials Chemistry

The key research topics explored in their work include:

  • Machine Learning in Bioinformatics
  • Smart Agriculture and AI
  • Text and Document Classification Technologies
  • Machine Learning and Data Classification
  • Topic Modeling
  • Natural Language Processing Techniques
  • Domain Adaptation and Few-Shot Learning

Manik Varma's publication record features research articles primarily published in venues such as arXiv (Cornell University), the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, ACM Transactions on Sensor Networks, IEEE Sensors Journal, and the Journal of Crop and Weed.

Recent publications include:

  • "RNNPool: Efficient Non-linear Pooling for RAM Constrained Inference" (2020), arXiv (Cornell University)
  • "Multi-modal Extreme Classification" (2022), 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • "One Size Does Not Fit All" (2021), ACM Transactions on Sensor Networks
  • "DeepXML: A Deep Extreme Multi-Label Learning Framework Applied to Short Text Documents" (2021), arXiv (Cornell University)
  • "Exploring the suitability of machine learning algorithms for crop yield forecasting using weather variables" (2022), Journal of Crop and Weed

The scientist has frequently collaborated with several coauthors including:

  • Sumeet Agarwal
  • Kunal Dahiya
  • Purushottam Kar
  • Deepak Saini
  • Kushal Dave

Manik Varma has been recognized as a Fellow of the Indian National Academy of Engineering (INAE), indicating a level of peer acknowledgment within the engineering community in India.

Best Publications

  • A Statistical Approach to Texture Classification from Single Images

    Manik Varma;Andrew Zisserman

  • Multiple kernels for object detection

    Andrea Vedaldi;Varun Gulshan;Manik Varma;Andrew Zisserman

  • A Statistical Approach to Texture Classification from Single Images

    Unknown

  • A Statistical Approach to Material Classification Using Image Patch Exemplars

    M. Varma;A. Zisserman

  • Learning The Discriminative Power-Invariance Trade-Off

    M. Varma;D. Ray

  • Texture classification: are filter banks necessary?

    M. Varma;A. Zisserman

  • CHARACTER RECOGNITION IN NATURAL IMAGES

    Teófilo Emídio de Campos;Bodla Rakesh Babu;Manik Varma

  • More generality in efficient multiple kernel learning

    Manik Varma;Bodla Rakesh Babu

  • Classifying Images of Materials: Achieving Viewpoint and Illumination Independence

    Manik Varma;Andrew Zisserman

  • Sparse local embeddings for extreme multi-label classification

    Kush Bhatia;Himanshu Jain;Purushottam Kar;Manik Varma

  • FastXML: a fast, accurate and stable tree-classifier for extreme multi-label learning

    Yashoteja Prabhu;Manik Varma

  • Extreme Multi-label Loss Functions for Recommendation, Tagging, Ranking & Other Missing Label Applications

    Himanshu Jain;Yashoteja Prabhu;Manik Varma

  • Multi-label learning with millions of labels: recommending advertiser bid phrases for web pages

    Rahul Agrawal;Archit Gupta;Yashoteja Prabhu;Manik Varma

  • Multiple Kernel Learning and the SMO Algorithm

    Zhaonan Sun;Nawanol Ampornpunt;Manik Varma;S.v.n. Vishwanathan

  • Parabel: Partitioned Label Trees for Extreme Classification with Application to Dynamic Search Advertising

    Yashoteja Prabhu;Anil Kag;Shrutendra Harsola;Rahul Agrawal

  • Resource-efficient machine learning in 2 KB RAM for the internet of things

    Ashish Kumar;Saurabh Goyal;Manik Varma

  • Locally Invariant Fractal Features for Statistical Texture Classification

    M. Varma;R. Garg

  • Large Scale Max-Margin Multi-Label Classification with Priors

    Bharath Hariharan;Lihi Zelnik-manor;Manik Varma;S.v.n. Vishwanathan

  • FastGRNN: A Fast, Accurate, Stable and Tiny Kilobyte Sized Gated Recurrent Neural Network

    Aditya Kusupati;Manish Singh;Kush Bhatia;Ashish Kumar

  • Learning to re-rank: query-dependent image re-ranking using click data

    Vidit Jain;Manik Varma

  • ProtoNN: compressed and accurate kNN for resource-scarce devices

    Chirag Gupta;Arun Sai Suggala;Ankit Goyal;Harsha Vardhan Simhadri

Frequent Co-Authors

Andrew Zisserman
Andrew Zisserman University of Oxford
Prateek Jain
Prateek Jain Google (United States)
Anish Arora
Anish Arora The Ohio State University
S. V. N. Vishwanathan
S. V. N. Vishwanathan Purdue University West Lafayette
C. V. Jawahar
C. V. Jawahar International Institute of Information Technology, Hyderabad
Jitendra Malik
Jitendra Malik University of California, Berkeley
Kentaro Toyama
Kentaro Toyama University of Michigan–Ann Arbor
Samy Bengio
Samy Bengio Apple (United States)
Andrea Vedaldi
Andrea Vedaldi University of Oxford
Thorsten Joachims
Thorsten Joachims Cornell University

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