Manik Varma

Distinguished Scientist & Vice President, Microsoft Research India
Adjunct Professor, Indian Institute of Technology Delhi
<manik@microsoft.com>

I am a Distinguished Scientist and Vice President at Microsoft Research India where my primary job is to not come in the way of a team carrying out research on various aspects of machine learning, information retrieval, natural language processing, systems and related areas. I also specialize in professing random gyan to my PhD students at IIT Delhi. For instance, I once proclaimed 2 KB (RAM) ought to be enough for everybody prompting the international media to cover my research and compare me to Bill Gates (unfair, I'm more handsome!). Similarly, John Langford and I once coined the term extreme classification and found that we had inadvertently started a new research area in machine learning. Today, by happenstance, extreme classification is thriving in both academia and industry with my algorithms making billions of predictions every day and generating billions of dollars in revenue (up to sign ambiguity). I have, on occasion, also been known to do something useful such as developing classifiers that have protected hundreds of millions of devices from viruses and malware. I have served as an associate editor-in-Chief of the IEEE TPAMI journal as well as a Senior Area Chair for most of the premiere machine learning, artificial intelligence and computer vision conferences. I have been elected a Fellow of the Indian Academies of Science (IASc, INSA and NASI), the Indian National Academy of Engineering (INAE) and the Association for Computing Machinery (ACM). I have also been awarded the Government of India's Shanti Swarup Bhatnagar Prize, have won the Microsoft Gold Star and Achievement awards, the WSDM 2019 Best Paper and BuildSys 2019 Best Paper Runner -up Prizes, won the PASCAL VOC Object Detection Challenge at ICCV 2009 and stood first in chicken chess tournaments and Pepsi drinking competitions. Upon learning about this, my son was overheard asking my daughter: How can Dad get a prize for computer science -- he can't even book an Uber by himself? Since then, I have felt it safest to describe myself as a failed physicist (BSc St. Stephen's College, David Raja Ram Prize), theoretician (BA Oxford, Rhodes Scholar), engineer (DPhil Oxford, University Scholar), mathematician (MSRI Berkeley, Post-doctoral Fellow) or astronomer (Visiting Miller Professor, UC Berkeley).

Research

I am interested in the following research areas

• Machine & Deep learning: Extreme classification, neural language modeling, multi-label learning, resource-efficient machine learning, machine learning for the Internet of Things, supervised learning.
• Information Retrieval: Computational advertising, web search, dense retrieval, large language models, recommender systems, query recommendation, personalization.
• Computer vision: Image search, object recognition, text recognition, texture classification.

Joining my group: I am actively looking to hire researchers and post-docs with a strong publication record in any area of Artificial Intelligence and Machine Learning at MSR India. I am also looking to hire exceptional engineers and data scientists. Finally, I am also looking for full time PhD students at IIT Delhi to work with me on extreme classification. Please e-mail your CV to me directly in addition to formally applying to IIT/Microsoft.

Projects, Internships and Research Fellowships: Unfortunately, I am unable to supervise projects or theses of students outside IIT Delhi. I am also unable to directly work with Research Fellows or interns at MSR India. . I get more e-mails on the topic than I can deal with, so I hope that you will accept my apologies and excuse me as I will not be able to respond to you.

Professional Activities

• Senior Area Chair (or equivalent): NeurIPS 2024, ICML 2024, NeurIPS 2023, ICML 2023, NeurIPS 2022, ICML 2022, AAAI 2022, AAAI 2021, IJCAI 2021, IJCAI 2019, AAAI 2018, IJCAI 2016 - AI on the Web, ICML 2016, ICML 2015, CVPR 2014, ACCV 2014, ICCV 2013, ICVGIP 2012, NIPS 2011, ICVGIP 2010


• Workshop Co-chair: Extreme Classification in Computer Vision, Dagstuhl Extreme Classification Seminars, Extreme Classification 2017, TinyML 2017, Extreme Classification 2016, Extreme Classification 2015, RecTech 2015, The MSRI Machine Learning Summer School, Extreme Classification 2013, WebVision 2012, The Mysore Park Computer Vision Workshop, The MSRI Computer Vision & Graphics Shindig, The Winter School on Machine Learning and Computer Vision


• Keynotes and Selected Invited Talks: Keynote at AJCAI 2023, Keynote at ACML 2022, ACM India Annual Event 2022 Keynote, Future of Search & Recommendation@The MSR Summit 2021, Keynote at the Amazon Research Days Conference 2020, XC@ICML 2020, MoL@IJCAI 2019, BigMine@KDD 2018, XMLC@WWW 2018, ACM India 2018 Summit, Keynote at the CODS+COMAD Conference 2018, Keynote at NASSCOM's Annual Tech Conference 2017, XC@NIPS 2016, DSI@KDD 2016, Keynote at the DICTA Conference 2015, Big Targets@ECML/PKDD 2015, X@ICML 2015, Budgeted ML@ICML 2015, Keynote at LSOLDM 2014, ISI Kolkata ML Unit Founder's Day Lecture 2014, IIIT Delhi Institute Lecture 2013, Keynote at the ICVGIP Conference 2012


• Media: An interview with the Asian Scientist Magazine, A blog post on an RNN based pooling operator for RAM constrained inference, Communications of the ACM article on extreme classification, A podcast episode on extreme classification, Living Science Media - Interview, Living Science Media - Talk, A blog post on training a hundred million classifiers, A blog post on a fast, accurate, stable & tiny gated RNN, A blog post on AI's big leap to tiny devices


• Teaching: 2009/CSL864   2013/CSV884   2016/SIV895   2018/COV878   2019/COV878  

Code

• The Extreme Classification Repository: Multi-label Datasets and Code
• The Microsoft Edge Machine Learning Library for Microcontrollers


• IRENE: Learning zero-shot classifiers for a hundred million classes
• OAK: Memory augmentation for efficient and accurate retrieval
• LEVER: Enhancing classification accuracy on tail labels by variance reduction
• DEXA: Adding auxiliary parameters to transformers for improved deep extreme classification
• Renee: Joint training of deep encoders + 1-vs-All extreme classifiers
• NGAME: A scalable and light-weight negative mining-aware mini-batching strategy for extreme classification
• Mufin: Learning multimodal embeddings of rich documents containing text and images using extreme classification
• SiameseXML: An extreme classification based generalization of Siamese Networks and Two Tower models
• DeepXML: A framework for jointly learning extreme classifiers with deep features based on RNNs, CNNs, fastText, etc.
• DECAF: Deep extreme classification with label features
• Slice: An extreme classifier for low-dimensional (deep learning) features that can scale to 100 million labels
• Parabel: Partitioned Label Trees for Extreme Classification
• SwiftXML: Extreme Classification with Label Features for Warm-start Tagging, Ranking and Recommendation
• PfastreXML: Propensity scored re-ranked FastXML
• SLEEC: Sparse Local Embeddings for Extreme multi-label Classification
• FastXML: A Fast, Accurate and Stable Tree Classifier for eXtreme Multi-label Learning
• M3L: Efficient Max Margin Multi-label Learning


• LDKL: Local Deep Kernel Learning for efficient non-linear prediction
• SPG-GMKL: Spectral Projected Gradient descent based optimization for Generalized Multiple Kernel Learning
• SMO-MKL: SMO based optimization for p-norm regularized Multiple Kernel Learning
• GMKL: Generalized Multiple Kernel Learning


• RNNPool: An RNN based pooling operator for RAM constrained inference
• MSCRNN: A Multi-scale Cascaded RNN
• GesturePod: Gesture recognition on microcontrollers
• FastGRNN: A Fast, Accurate, Stable & Tiny Gated RNN
• ProtoNN: A compact kNN algorithm for microcontrollers
• Bonsai: A compact tree classifier for microcontrollers

Datasets

• MS MARCO Web Search: a Large-scale Information-rich Web Dataset with Millions of Real Click Labels
• The Extreme Classification Repository: Multi-label Datasets and Code
• The Chars74K Dataset: Character Recognition in Natural Images and an associated Julia tutorial and Kaggle competition (I have no idea how "Google" crept into the dataset name)
• CUReT: The Cropped Columbia-Utrecht Texture Classification Dataset & Associated Filterbanks
• ORCAS-800K: A subset of the original ORCAS dataset mapping user queries on the Bing search engine to the relevant subset of 800K web URLs.
• The GesturePod Dataset
• The MSCRNN Radar Dataset

Current PhD Students

• Kunal Dahiya
• Sonu Mehta
• Anshul Mittal
• Suchith Prabhu

Some Former Students

• Ananye Agarwal (PhD student at CMU, BTech IIT Delhi)
• Kush Bhatia (PhD student at Berkeley, MSRI Research Fellow)
• Rahul Garg (Senior Research Scientist at Google Mountain View, PhD UW, BTech IIT Delhi)
• Varun Gulshan (Staff Research Scientist at Google Bangalore, DPhil Oxford, BTech IIT Delhi)
• Nilesh Gupta (PhD student at UT Austin, MSRI Research Fellow)
• Saurabh Gupta (Assistant Professor at UIUC, PhD Berkeley, BTech IIT Delhi)
• Bharath Hariharan (Assistant Professor at Cornell, PhD Berkeley, BTech IIT Delhi)
• Ashesh Jain (Head of Perception at Lyft, PhD Cornell, BTech IIT Delhi)
• Himanshu Jain (Software Engineer at Google New York, PhD IIT Delhi)
• Suraj Jain (Senior Applied Scientist at Microsoft Redmond, MSRI Research Fellow)
• Anil Kag (PhD student at BU, MSRI Research Fellow)
• Ashish Kumar (PhD student at Berkeley, MSRI Research Fellow)
• Aditya Kusupati (PhD student at UW, MSRI Research Fellow)
• Adarsh Prasad (PhD student at CMU, BTech IIT Delhi)
• Himanshu Gaurav Singh (PhD student at Berkeley, BTech IIT Delhi)
• Manish Singh (PhD student at MIT, BTech IIT Delhi)
• Yashoteja Prabhu (Senior Researcher at MSR India, PhD IIT Delhi)
• Deepak Vasisht (Assistant Professor at UIUC, PhD MIT, BTech IIT Delhi)

Publications

• S. Yadav, D. Saini, A. Buvanesh, B. Paliwal, K. Dahiya, S. Asokan, Y. Prabhu, J. Jiao and M. Varma. Extreme meta-classification for large-scale zero-shot retrieval. In Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Barcelona, Spain, August 2024.
  Bibtex source | Abstract | Download in pdf format | Code | Extreme Classification Repository



• S. Mohan, D. Saini, A. Mittal, S. R. Chowdhury, B. Paliwal, J. Jiao, M. Gupta and M. Varma. OAK: Enriching document representations using auxiliary knowledge for extreme classification. In Proceedings of the International Conference on Machine Learning, Vienna, Austria, July 2024.
  Bibtex source | Abstract | Download in pdf format | Code | Extreme Classification Repository



• A. Buvanesh, R. Chand, J. Prakash, B. Paliwal, M. Dhawan, N. Madan, D. Hada, V. Jain, S. Mehta, Y. Prabhu, M. Gupta, R. Ramjee and M. Varma. Enhancing tail performance in extreme classifiers by label variance reduction. In Proceedings of the International Conference on Learning Representations, Vienna, Austria, May 2024.
  Bibtex source | Abstract | Download in pdf format | Code | Extreme Classification Repository



• Q. Chen, X. Geng, C. Rosset, C. Buractaon, J. Lu, T. Shen, K. Zhou, C. Xiong, Y. Gong, P. Bennett, N. Craswell, X. Xie, F. Yang, B. Tower, N. Rao, A. Dong, W. Jiang, Z. Liu, M. Li, C. Liu, Z. Li, R. Majumder, J. Neville, A. Oakley, K. M. Risvik, H. V. Simhadri, M. Varma, Y. Wang, L. Yang, M. Yang and C. Zhang. MS MARCO Web Search: a large-scale information-rich web dataset with millions of real click labels. In Companion Proceedings of the ACM Web Conference, Singapore, May 2024.
  Bibtex source | Abstract | Download in pdf format | Dataset



• K. Dahiya, S. Yadav, S. Sondhi, D. Saini, S. Mehta, J. Jiao, S. Agarwal, P. Kar and M. Varma. Deep encoders with auxiliary parameters for extreme classification. In Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Long Beach, California, August 2023.
  Bibtex source | Abstract | Download in pdf format | Code | The ORCAS-800K Dataset | Extreme Classification Repository | Sachin Yadav's DEXA page



• H. Vemuri, S. Agrawal, S. Mittal, D. Saini, A. Soni, A. V. Sambasivan, W. Lu, Y. Wang, M. Parsana, P. Kar and M. Varma. Personalized retrieval over millions of items. In Proceedings of the ACM SIGIR Conference on Research and Development in Information Retrieval, Taipei, Taiwan, July 2023.
  Bibtex source | Abstract | Download in pdf format | Code



• V. Jain, J. Prakash, D. Saini, J. Jiao, R. Ramjee and M. Varma. Renee: End-to-end training of extreme classification models. In Proceedings of the Conference on Machine Learning and Systems, Miami, Florida, June 2023.
  Bibtex source | Abstract | Download in pdf format | Code | Extreme Classification Repository



• K. Dahiya, N. Gupta, D. Saini, A. Soni, Y. Wang, K. Dave, J. Jiao, K. Gururaj, P. Dey, A. Singh, D. Hada, V. Jain, B. Paliwal, A. Mittal, S. Mehta, R. Ramjee, S. Agarwal, P. Kar and M. Varma. NGAME: Negative mining-aware mini-batching for extreme classification. In Proceedings of the ACM International Conference on Web Search and Data Mining, Singapore, March 2023.
  Bibtex source | Abstract | Download in pdf format | Code | Extreme Classification Repository



• A. Mittal, K. Dahiya, S. Malani, J. Ramaswamy, S. Kuruvilla, J. Ajmera, K. Chang, S. Agrawal, P. Kar and M. Varma. Multimodal extreme classification. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, Louisiana, June 2022.
  Bibtex source | Abstract | Download in pdf format | Code | Extreme Classification Repository



• N. Gupta, S. Bohra, Y. Prabhu, S. Purohit and M. Varma. Generalized zero-Shot extreme multi-label learning. In Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Singapore, August 2021.
  Bibtex source | Abstract | Download in pdf format | Code | Extreme Classification Repository



• K. Dahiya, A. Agarwal, D. Saini, K. Gururaj, J. Jiao, A. Singh, S. Agarwal, P. Kar and M. Varma. SiameseXML: Siamese networks meet extreme classifiers with 100M labels. In Proceedings of the International Conference on Machine Learning, July 2021.
  Bibtex source | Abstract | Download in pdf format | Code | Extreme Classification Repository



• D. Saini, A. K. Jain, K. Dave, J. Jiao, A. Singh, R. Zhang and M. Varma. GalaXC: Graph neural networks with labelwise attention for extreme classification. In Proceedings of the ACM International World Wide Web Conference, Ljubljana, Slovenia, April 2021.
  Bibtex source | Abstract | Download in pdf format | Code | Extreme Classification Repository



• A. Mittal, N. Sachdeva, S. Agrawal, S. Agarwal, P. Kar and M. Varma. ECLARE: Extreme classification with label graph correlations. In Proceedings of the ACM International World Wide Web Conference, Ljubljana, Slovenia, April 2021.
  Bibtex source | Abstract | Download in pdf format | Code | Extreme Classification Repository



• K. Dahiya, D. Saini, A. Mittal, A. Shaw, K. Dave, A. Soni, H. Jain, S. Agarwal and M. Varma. DeepXML: A deep extreme multi-Label learning framework applied to short text documents. In Proceedings of the ACM International Conference on Web Search and Data Mining, Jerusalem, Israel, March 2021.
  Bibtex source | Abstract | Download in pdf format | Code | Extreme Classification Repository



• A. Mittal, K. Dahiya, S. Agrawal, D. Saini, S. Agarwal, P. Kar and M. Varma. DECAF: Deep extreme classification with label features. In Proceedings of the ACM International Conference on Web Search and Data Mining, Jerusalem, Israel, March 2021.
  Bibtex source | Abstract | Download in pdf format | Code | Extreme Classification Repository



• D. Roy, S. Srivastava, A. Kusupati, P. Jain, M. Varma and A. Arora. One size does not fit all: Multi-scale, cascaded RNNs for radar classification. ACM Transactions on Sensor Networks, 17(2), January 2021.
  Bibtex source | Abstract | Download in pdf format | Code | Dataset



• O. Saha, A. Kusupati, H. V. Simhadri, M. Varma and P. Jain. RNNPool: Efficient non-linear pooling for RAM constrained inference. In Advances in Neural Information Processing Systems, Vancouver, Canada, December 2020.
  Bibtex source | Abstract | Download in pdf format | Code | Spotlight Video | Blog



• Y. Prabhu, A. Kusupati, N. Gupta and M. Varma. Extreme regression for dynamic search advertising. In Proceedings of the ACM International Conference on Web Search and Data Mining, Houston, Texas, February 2020.
  Bibtex source | Abstract | Download in pdf format



• M. Varma. Extreme classification. Communications of the ACM, 62(11):44--45, November 2019.
  Bibtex source | Download in pdf format



• D. Roy, S. Srivastava, A. Kusupati, P. Jain, M. Varma and A. Arora. One size does not fit all: Multi-scale, cascaded RNNs for radar classification. In Proceedings of the ACM International Conference on Systems for Energy-efficient Buildings, Cities and Transportation, New York, New York, November 2019.
  Bibtex source | Abstract | Download in pdf format | Code | Dataset



• S. G. Patil, D. K. Dennis, C. Pabbaraju, N. Shaheer, H. V. Simhadri, V. Seshadri, M. Varma and P. Jain. GesturePod: Enabling on-device gesture-based interaction for white cane users. In Proceedings of the ACM Symposium on User Interface Software and Technology, New Orleans, Louisiana, October 2019.
  Bibtex source | Abstract | Download in pdf format | Code | Dataset | Video Preview



• S. Bengio, K. Dembczynski, T. Joachims, M. Kloft and M. Varma. Extreme Classification (Dagstuhl Seminar 18291). Dagstuhl Reports, 8(7):62--80, 2019.
  Bibtex source | Abstract | Download in pdf format



• H. Jain, V. Balasubramanian, B. Chunduri and M. Varma. Slice: Scalable linear extreme classifiers trained on 100 million labels for Related Searches. In Proceedings of the ACM International Conference on Web Search and Data Mining, Melbourne, Australia, February 2019.
  Bibtex source | Abstract | Download in pdf format | Code | Extreme Classification Repository | Blog | Podcast



• A. Kusupati, M. Singh, K. Bhatia, A. Kumar, P. Jain and M. Varma. FastGRNN: A fast, accurate, stable and tiny kilobyte sized gated Recurrent Neural Network. In Advances in Neural Information Processing Systems, Montreal, Canada, December 2018.
  Bibtex source | Abstract | Download in pdf format | Code | Short Video | Blog



• Y. Prabhu, A. Kag, S. Harsola, R. Agrawal and M. Varma. Parabel: Partitioned label trees for extreme classification with application to Dynamic Search Advertising. In Proceedings of the ACM International World Wide Web Conference, Lyon, France, April 2018.
  Bibtex source | Abstract | Download in pdf format | Code | Extreme Classification Repository | Podcast



• Y. Prabhu, A. Kag, S. Gopinath, K. Dahiya, S. Harsola, R. Agrawal and M. Varma. Extreme multi-label learning with label features for warm-start tagging, ranking and recommendation. In Proceedings of the ACM International Conference on Web Search and Data Mining, Los Angeles, California, February 2018.
  Bibtex source | Abstract | Download in pdf format | Code | Extreme Classification Repository



• A. Kumar, S. Goyal and M. Varma. Resource-efficient machine learning in 2 KB RAM for the Internet of Things. In Proceedings of the International Conference on Machine Learning, Sydney, Australia, August 2017.
  Bibtex source | Abstract | Download in pdf format | Code | Blog



• C. Gupta, A. Suggala, A. Gupta, H. Simhadri, B. Paranjape, A. Kumar, S. Goyal, R. Udupa, M. Varma and P. Jain. ProtoNN: Compressed and accurate kNN for resource-scarce devices. In Proceedings of the International Conference on Machine Learning, Sydney, Australia, August 2017.
  Bibtex source | Abstract | Download in pdf format | Code



• H. Jain, Y. Prabhu and M. Varma. Extreme multi-label loss functions for recommendation, tagging, ranking & other missing label applications. In Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining, San Francisco, California, August 2016.
  Bibtex source | Abstract | Download in pdf format | Code | Extreme Classification Repository | Talk



• K. Bhatia, H. Jain, P. Kar, M. Varma and P. Jain. Sparse local embeddings for extreme multi-label classification. In Advances in Neural Information Processing Systems, Montreal, Canada, December 2015.
  Bibtex source | Abstract | Download in pdf format | Code | Extreme Classification Repository



• Y. Prabhu and M. Varma. FastXML: A fast, accurate and stable tree-classifier for extreme multi-label learning. In Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining, New York, New York, August 2014.
  Bibtex source | Abstract | Download in pdf format | Code | Extreme Classification Repository | Slides | Talk on Extreme Classification & FastXML



• D. Vasisht, A. Damianou, M. Varma and A. Kapoor. Active learning for sparse Bayesian multi-label classification. In Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining, New York, New York, August 2014.
  Bibtex source | Abstract | Download in pdf format



• P. Jawanpuria, M. Varma and J. Saketha Nath. On p-norm path following in multiple kernel learning for non-linear feature selection. In Proceedings of the International Conference on Machine Learning, Beijing, China, June 2014.
  Bibtex source | Abstract | Download in pdf format



• C. Jose, P. Goyal, P. Aggrwal and M. Varma. Local deep kernel learning for efficient non-linear SVM prediction. In Proceedings of the International Conference on Machine Learning, Atlanta, Georgia, June 2013.
  Bibtex source | Abstract | Download in pdf format | Code | Slides



• R. Agrawal, A. Gupta, Y. Prabhu and M. Varma. Multi-label learning with millions of labels: Recommending advertiser bid phrases for web pages. In Proceedings of the International World Wide Web Conference, Rio de Janeiro, Brazil, May 2013.
  Bibtex source | Abstract | Download in pdf format | Slides



• A. Jain, S. V. N. Vishwanathan and M. Varma. SPG-GMKL: Generalized multiple kernel learning with a million kernels. In Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Beijing, China, August 2012.
  Bibtex source | Abstract | Download in pdf format | Code



• B. Hariharan, S. V. N. Vishwanathan and M. Varma. Efficient max-margin multi-label classification with applications to zero-shot learning. Machine Learning Journal, 88(1):127--155, 2012.
  Bibtex source | Abstract | Download in pdf format | Code



• V. Jain and M. Varma. Learning to re-rank: Query-dependent image re-ranking using click data. In Proceedings of the International World Wide Web Conference, Hyderabad, India, March 2011.
  Bibtex source | Abstract | Download in pdf format | Slides



• S. V. N. Vishwanathan, Z. Sun, N. Theera-Ampornpunt and M. Varma. Multiple kernel learning and the SMO algorithm. In Advances in Neural Information Processing Systems, Vancouver, B. C., Canada, December 2010.
  Bibtex source | Abstract | Download in pdf format | Code | Spotlight



• B. Hariharan, L. Zelnik-Manor, S. V. N. Vishwanathan and M. Varma. Large scale max-margin multi-label classification with priors. In Proceedings of the International Conference on Machine Learning, Haifa, Israel, June 2010.
  Bibtex source | Abstract | Download in pdf format | Code



• M. Varma and A. Zisserman. A statistical approach to material classification using image patch exemplars. IEEE Transactions on Pattern Analysis and Machine Intelligence , 31(11):2032--2047, November 2009.
  Bibtex source | Abstract | Download in pdf format



• A. Vedaldi, V. Gulshan, M. Varma and A. Zisserman. Multiple kernels for object detection. In Proceedings of the International Conference on Computer Vision, Kyoto, Japan, September 2009.
  Bibtex source | Abstract | Download in pdf format | Code



• M. Varma and B. R. Babu. More generality in efficient multiple kernel learning. In Proceedings of the International Conference on Machine Learning, Montreal, Canada, pages 1065--1072, June 2009.
  Bibtex source | Abstract | Download in pdf format | Code



• T. E. de Campos, B. R. Babu and M. Varma. Character recognition in natural images. In Proceedings of the International Conference on Computer Vision Theory and Applications, Lisbon, Portugal, February 2009.
  Bibtex source | Abstract | Download in pdf format | Download English and Kannada datasets



• M. Varma and D. Ray. Learning the discriminative power-invariance trade-off. In Proceedings of the IEEE International Conference on Computer Vision, Rio de Janeiro, Brazil, October 2007.
  Bibtex source | Abstract | Download in pdf format | www | Code | Errata
  Please see the errata regarding the Caltech experiments



• M. Varma and R. Garg. Locally invariant fractal features for statistical texture classification. In Proceedings of the IEEE International Conference on Computer Vision, Rio de Janeiro, Brazil, October 2007.
  Bibtex source | Abstract | Download in pdf format



• N. Adabala and M. Varma and K. Toyama. Computer aided generation of stylized maps. Computer Animation and Virtual Worlds, 18(2):133--140, May 2007.
  Bibtex source | Abstract | Download in pdf format



• M. Varma and A. Zisserman. A statistical approach to texture classification from single images. International Journal of Computer Vision: Special Issue on Texture Analysis and Synthesis, 62(1--2):61--81, April 2005.
  Bibtex source | Abstract | Download in pdf format



• M. Varma and A. Zisserman. Unifying statistical texture classification frameworks. Image and Vision Computing, 22(14):1175--1183, December 2004.
  Bibtex source | Abstract | Download in pdf format



• M. Varma. Statistical approaches to texture classification. DPhil Thesis, University of Oxford, October 2004.
  Bibtex source | Abstract | Download in pdf format



• M. Varma and A. Zisserman. Estimating illumination direction from texturede images. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Washington, DC, volume 1, pages 179--186, June 2004.
  Bibtex source | Abstract | Download in pdf format



• M. Varma and A. Zisserman. Texture classification: Are filter banks necessary? In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Madison, Wisconsin, volume 2, pages 691--698, June 2003.
  Bibtex source | Abstract | Download in pdf format



• M. Varma and A. Zisserman. Statistical approaches to material classification. In Proceedings of the Indian Conference on Computer Vision, Graphics and Image Processing, Ahmedabad, India, pages 167--172, December 2002.
  Bibtex source | Abstract | Download in pdf format



• M. Varma and A. Zisserman. Classifying materials from images: to cluster or not to cluster? In Proceedings of the 2nd International Workshop on Texture Analysis and Synthesis, Copenhagen, Denmark, pages 139--144, June 2002.
  Bibtex source | Abstract



• M. Varma and A. Zisserman. Classifying images of materials: Achieving viewpoint and illumination independence. In Proceedings of the 7th European Conference on Computer Vision, Copenhagen, Denmark, volume 3, pages 255--271. Springer-Verlag, May 2002.
  Bibtex source | Abstract | Download in pdf format



• M. Varma and V. S. Varma. Computer simulation of evolution. In Proceedings of the GIREP-ICPE International Conference, Ljubljana, Slovenia, pages 138--150, August 1996.
  Bibtex source