vikas.sindhwani.org

Vikas Sindhwani

Machine Learning and Intelligence. My interests range from core mathematical foundations and algorithm design aspects of machine intelligence, to end-to-end implementation considerations in building robust and scalable learning systems. I am particularly interested in Machine Perception and AI problems involving Computer Vision, Natural Language Processing and Speech Recognition. Google Research, NY. NIPS 2014 Workshop on Reimannian Geometry in Machine Learning, Statistics and Computer Vision.

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Vikas Sindhwani | vikas.sindhwani.org Reviews

<META> DESCRIPTION

Machine Learning and Intelligence. My interests range from core mathematical foundations and algorithm design aspects of machine intelligence, to end-to-end implementation considerations in building robust and scalable learning systems. I am particularly interested in Machine Perception and AI problems involving Computer Vision, Natural Language Processing and Speech Recognition. Google Research, NY. NIPS 2014 Workshop on Reimannian Geometry in Machine Learning, Statistics and Computer Vision.

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1 research

2 papers

3 talks

4 summer students

5 software

6 svmlin

7 manifold regularization

8 skylark

9 vikas sindhwani

10 research interests

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research,papers,talks,summer students,software,svmlin,manifold regularization,skylark,vikas sindhwani,research interests,vikassindhwani@gmail com,linkedin profile,publications,co organizer,source

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Vikas Sindhwani | vikas.sindhwani.org Reviews

https://vikas.sindhwani.org

Machine Learning and Intelligence. My interests range from core mathematical foundations and algorithm design aspects of machine intelligence, to end-to-end implementation considerations in building robust and scalable learning systems. I am particularly interested in Machine Perception and AI problems involving Computer Vision, Natural Language Processing and Speech Recognition. Google Research, NY. NIPS 2014 Workshop on Reimannian Geometry in Machine Learning, Statistics and Computer Vision.

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vikas.sindhwani.org

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Selected Invited and Conference Talks

http://vikas.sindhwani.org/talks.html

Selected Invited and Conference Talks. Real-time Learning and Inference on Emerging Mobile Systems. IEEE Signal Processing Society Summer School on Big Data and Machine Learning, 2016. Kernels, Random Embeddings and Deep Learning. William Pierson Field Lecture, Princeton University, 2016. Real-time On-device Learning and Inference with Structured Transforms. Invited session on Statistical Machine Learning and Optimization, CISS 2016. Structured Transforms for Small Footprint Deep Learning. Page generated...

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Vikas Sindhwani

http://vikas.sindhwani.org/interns.html

I have mentored several excellent graduate students on various summer internship projects:. Becca Roelofs (UC Berkeley). Learning Compact RNNs, ICASSP 2015. Making Random Fourier Features more efficient using QMC techniques (ICML 2014). Random Laplace Features for Computer Vision Problems (CVPR 2014). With IBM speech group). Scaling up Kernel methods for Speech Recognition (Best Student Paper at ICASSP 2014! Abhishek Kumar (Univ. of Maryland; now at IBM Research). Ankan Saha (UChicago; now at LinkedIn).

3

Publications

http://vikas.sindhwani.org/papers.html

Ldquo;We can only see a short distance ahead, but we can see plenty there that needs to be done.”. Ndash; Alan Turing, 1950. Ldquo;It is still an unending source of surprise for me to see how a few scribbles on a blackboard or on a sheet of paper could change the course of human affairs.”. Ndash; Stanislaw Ulam. Quasi-Monte Carlo Feature Maps for Shift-Invariant Kernels. Haim Avron, Vikas Sindhwani, Jiyan Yang, Michael Mahoney. Journal of Machine Learning Research. K Choromanski and V. Sindhwani. A Kumar...

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Vikas Sindhwani

http://vikas.sindhwani.org/index.html

Machine Learning and Intelligence. My interests range from core mathematical foundations and algorithm design aspects of machine intelligence, to end-to-end implementation considerations in building robust and scalable learning systems. I am particularly interested in Machine Perception and AI problems involving Computer Vision, Natural Language Processing and Speech Recognition. My current interests are in perception, learning and control problems arising in Robotics. Google Research, NY.

5

Manifold Regularization

http://vikas.sindhwani.org/manifoldregularization.html

Java Applet for Laplacian SVMs. Click on "Load toy1". The large colored points are labeled examples and the small white points are unlabeled examples. Press "Run". You will see the decision surface obtained by an SVM using just the labeled examples. Now change the options in the text area below to. T 2 -c 100 -l 1000" and press "Run". This is the decision boundary obtained using Laplacian SVM. You can upload your 2d dataset or interactively construct a dataset by using the "class:1" button.

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Organizers

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In machine learning, statistics and computer vision. Istituto Italiano di Tecnologia, Genova, Italy). Minh Ha Quang is Researcher in Pattern Analysis and Computer Vision at the Istituto Italiano di Tecnologia (IIT) in Genova, Italy. He received his PhD in Mathematics from Brown University, USA, in May 2006. His research interests are in applied and computational functional analysis, machine learning, and statistics, with applications in computer vision and image processing.

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vikas.sindhwani.org

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Machine Learning and Intelligence. My interests range from core mathematical foundations and algorithm design aspects of machine intelligence, to end-to-end implementation considerations in building robust and scalable learning systems. I am particularly interested in Machine Perception and AI problems involving Computer Vision, Natural Language Processing and Speech Recognition. Google Research, NY. NIPS 2014 Workshop on Reimannian Geometry in Machine Learning, Statistics and Computer Vision.

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