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Rahul Garg

Researcher at Indian Institute of Technology Delhi

Publications -  104
Citations -  3219

Rahul Garg is an academic researcher from Indian Institute of Technology Delhi. The author has contributed to research in topics: Computer science & Geology. The author has an hindex of 26, co-authored 87 publications receiving 3052 citations. Previous affiliations of Rahul Garg include eBay & Indian Institutes of Technology.

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Proceedings ArticleDOI

An Overview of the BlueGene/L Supercomputer

N. R. Adiga, +114 more
TL;DR: An overview of the BlueGene/L Supercomputer, a massively parallel system of 65,536 nodes based on a new architecture that exploits system-on-a-chip technology to deliver target peak processing power of 360 teraFLOPS (trillion floating-point operations per second).
Patent

Differential rewards with dynamic user profiling

TL;DR: In this paper, a methodology and system allows a plurality of reward scheme owners to give differential rewards to various users based on the user profile, and the reward distribution agent may be a seller, a manufacturer, a sales promotion agent or even an intermediary.
Proceedings ArticleDOI

Gradient descent with sparsification: an iterative algorithm for sparse recovery with restricted isometry property

TL;DR: The Matlab implementation of GraDeS (Gradient Descent with Sparsification) outperforms previously proposed algorithms like Subspace Pursuit, StOMP, OMP, and Lasso by an order of magnitude and uncovered cases where L1-regularized regression (Lasso) fails but GraDeS finds the correct solution.
Patent

Generation, distribution, storage, redemption, validation and clearing of electronic coupons

TL;DR: In this article, a method and system generates, distributes, stores, redeems, validates and clears electronic manufacturer coupons and electronic store coupons and a coupon mint generates unforgable blank digital coupons.
Journal ArticleDOI

Prediction and interpretation of distributed neural activity with sparse models

TL;DR: This work applies a recently introduced regularized regression technique, the Elastic Net, to the analysis of the PBAIC 2007 competition data, and finds that this method produces highly predictive models of fMRI data that provide evidence for the distributed nature of neural function.