Search Results for author: Rakesh Shivanna

Found 7 papers, 1 papers with code

Nonlinear Initialization Methods for Low-Rank Neural Networks

no code implementations2 Feb 2022 Kiran Vodrahalli, Rakesh Shivanna, Maheswaran Sathiamoorthy, Sagar Jain, Ed H. Chi

We propose a novel low-rank initialization framework for training low-rank deep neural networks -- networks where the weight parameters are re-parameterized by products of two low-rank matrices.

Understanding and Improving Knowledge Distillation

no code implementations10 Feb 2020 Jiaxi Tang, Rakesh Shivanna, Zhe Zhao, Dong Lin, Anima Singh, Ed H. Chi, Sagar Jain

Knowledge Distillation (KD) is a model-agnostic technique to improve model quality while having a fixed capacity budget.

Knowledge Distillation Model Compression

How Many Pairwise Preferences Do We Need to Rank A Graph Consistently?

no code implementations6 Nov 2018 Aadirupa Saha, Rakesh Shivanna, Chiranjib Bhattacharyya

Our proposed algorithm, {\it Pref-Rank}, predicts the underlying ranking using an SVM based approach over the chosen embedding of the product graph, and is the first to provide \emph{statistical consistency} on two ranking losses: \emph{Kendall's tau} and \emph{Spearman's footrule}, with a required sample complexity of $O(n^2 \chi(\bar{G}))^{\frac{2}{3}}$ pairs, $\chi(\bar{G})$ being the \emph{chromatic number} of the complement graph $\bar{G}$.

Spectral Norm Regularization of Orthonormal Representations for Graph Transduction

no code implementations NeurIPS 2015 Rakesh Shivanna, Bibaswan K. Chatterjee, Raman Sankaran, Chiranjib Bhattacharyya, Francis Bach

We propose an alternative PAC-based bound, which do not depend on the VC dimension of the underlying function class, but is related to the famous Lov\'{a}sz~$\vartheta$ function.

Learning on graphs using Orthonormal Representation is Statistically Consistent

no code implementations NeurIPS 2014 Rakesh Shivanna, Chiranjib Bhattacharyya

This, for the first time, relates labelled sample complexity to graph connectivity properties, such as the density of graphs.

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