Search Results - "Varma, Rohan"

Representations of piecewise smooth signals on graphs by Siheng Chen, Varma, Rohan, Singh, Aarti, Kovacevic, Jelena

“…We study representations of piecewise-smooth signals on graphs. We first define classes for smooth, piecewise-constant, and piecewise-smooth graph signals,…”
Get full text

Discrete Signal Processing on Graphs: Sampling Theory by Siheng Chen, Varma, Rohan, Sandryhaila, Aliaksei, Kovacevic, Jelena

“…We propose a sampling theory for signals that are supported on either directed or undirected graphs. The theory follows the same paradigm as classical sampling…”
Get full text

Passive and Active Sampling for Piecewise-Smooth Graph Signals by Varma, Rohan, Kovacevic, Jelena

“…In this work, we study the sampling of piecewise smooth- graph signals that exhibit an inhomogeneous level of smoothness over the graph and are characterized…”
Get full text

Smooth Signal Recovery on Product Graphs by Varma, Rohan, Kovacevic, Jelena

“…Product graphs are a useful way to model richer forms of graph-structured data that can be multi-modal in nature. In this work, we study the reconstruction or…”
Get full text

Signal Recovery on Graphs: Fundamental Limits of Sampling Strategies by Chen, Siheng, Varma, Rohan, Singh, Aarti, Kovacevic, Jelena

“…This paper builds theoretical foundations for the recovery of a newly proposed class of smooth graph signals, approximately bandlimited graph signals, under…”
Get full text

Vector-Valued Graph Trend Filtering With Non-Convex Penalties by Varma, Rohan, Lee, Harlin, Kovacevic, Jelena, Chi, Yuejie

“…This article studies the denoising of piecewise smooth graph signals that exhibit inhomogeneous levels of smoothness over a graph, where the value at each node…”
Get full text

Rakeness-Based Compressed Sensing of Multiple-Graph Signals for IoT Applications by Mangia, Mauro, Pareschi, Fabio, Varma, Rohan, Rovatti, Riccardo, Kovacevic, Jelena, Setti, Gianluca

“…Signals on multiple graphs may model IoT scenarios consisting of a local wireless sensor network performing sets of acquisitions that must be sent to a central…”
Get full text

Reconstructing cancer drug response networks using multitask learning by Ruffalo, Matthew, Stojanov, Petar, Pillutla, Venkata Krishna, Varma, Rohan, Bar-Joseph, Ziv

“…Translating in vitro results to clinical tests is a major challenge in systems biology. Here we present a new Multi-Task learning framework which integrates…”
Get full text

The Relative performance of Security protocols and attacks in Wireless Sensor Network by Dobhal, Karishma, Varma, Rohan, Barthwal, Varun, Rauthan, M.M.S.

“…Wireless Sensor Network is organized by arranging a huge number of sensor nodes in a region to the investigation of normally remote locations. A Wireless…”
Get full text

Improving Graph Trend Filtering with Non-convex Penalties by Varma, Rohan, Lee, Harlin, Chi, Yuejie, Kovacevic, Jelena

“…In this paper, we study the denoising of piecewise smooth graph signals that exhibit inhomogeneous levels of smoothness over a graph. We extend the graph trend…”
Get full text

Exploiting Structure in Data: Sampling and Signal Processing on Graphs by Varma, Rohan Anilkumar

“…With the explosive growth of information and communication, data is being generated at an unprecedented rate from various sources, including multimedia, sensor…”
Get full text

Abstract 5333: Combining chemoproteomics with machine learning identifies functionally active covalent fragments for hard-to-drug cancer drivers by Hermann, Johannes C., Everley, Robert, Marholz, Laura, Berberich, Matthew, Yang, Tzu-Yi, Chao, Yu-Hsin, Awol, Abduselam K., Shaghafi, Michael, Yoon, Han, Varma, Rohan, Stein, Reed, Krug, Karsten, Lachtara, Emily, Erlanson, Daniel, Varma, Chris, Webster, Kevin R.

“…Abstract a) Many cancer drivers are considered “undruggable” and without targeted treatments because they lack binding sites for conventional small molecules…”
Get full text

Sampling Theory for Graph Signals on Product Graphs by Varma, Rohan, Kovačević, Jelena

“…In this paper, we extend the sampling theory on graphs by constructing a framework that exploits the structure in product graphs for efficient sampling and…”
Get full text

SAMPLING THEORY FOR GRAPH SIGNALS ON PRODUCT GRAPHS by Varma, Rohan A., Kovacevic, Jelena

“…In this paper, we extend the sampling theory on graphs by constructing a framework that exploits the structure in product graphs for efficient sampling and…”
Get full text

Random Sampling for Bandlimited Signals on Product Graphs by Varma, Rohan, Kovacevic, Jelena

“…In this work, we construct a structured framework for the efficient random sampling and recovery of bandlimited graph signals that lie on product graphs…”
Get full text

Signal recovery on graphs: Random versus experimentally designed sampling by Siheng Chen, Varma, Rohan, Singh, Aarti, Kovacevic, Jelena

“…We study signal recovery on graphs based on two sampling strategies: random sampling and experimentally designed sampling. We propose a new class of smooth…”
Get full text

Vector-Valued Graph Trend Filtering with Non-Convex Penalties by Varma, Rohan, Lee, Harlin, Kovačević, Jelena, Chi, Yuejie

“…IEEE Transactions on Signal and Information Processing over Networks, vol. 6, pp. 48-62, 2020 This work studies the denoising of piecewise smooth graph signals…”
Get full text

A statistical perspective of sampling scores for linear regression by Siheng Chen, Varma, Rohan, Singh, Aarti, Kovacevic, Jelena

“…In this paper, we consider a statistical problem of learning a linear model from noisy samples. Existing work has focused on approximating the least squares…”
Get full text

Spectrum-blind signal recovery on graphs by Varma, Rohan, Siheng Chen, Kovacevic, Jelena

“…We consider the problem of recovering a graph signal, sparse in the graph spectral domain from a few number of samples. In contrast to most previous work on…”
Get full text

PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel by Zhao, Yanli, Gu, Andrew, Varma, Rohan, Luo, Liang, Huang, Chien-Chin, Xu, Min, Wright, Less, Shojanazeri, Hamid, Ott, Myle, Shleifer, Sam, Desmaison, Alban, Balioglu, Can, Damania, Pritam, Nguyen, Bernard, Chauhan, Geeta, Hao, Yuchen, Mathews, Ajit, Li, Shen

“…It is widely acknowledged that large models have the potential to deliver superior performance across a broad range of domains. Despite the remarkable progress…”
Get full text