Saurabh Agarwal | DeepAI

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∙ 05/04/2023

Cuttlefish: Low-Rank Model Training without All the Tuning

Recent research has shown that training low-rank neural networks can eff...

0 Hongyi Wang, et al. ∙

∙ 02/24/2022

BagPipe: Accelerating Deep Recommendation Model Training

Deep learning based recommendation models (DLRM) are widely used in seve...

3 Saurabh Agarwal, et al. ∙

∙ 03/05/2021

Pufferfish: Communication-efficient Models At No Extra Cost

To mitigate communication overheads in distributed model training, sever...

10 Hongyi Wang, et al. ∙

∙ 02/28/2021

On the Utility of Gradient Compression in Distributed Training Systems

Rapid growth in data sets and the scale of neural network architectures ...

16 Saurabh Agarwal, et al. ∙

∙ 02/02/2021

AutoFreeze: Automatically Freezing Model Blocks to Accelerate Fine-tuning

With the rapid adoption of machine learning (ML), a number of domains no...

12 Yuhan Liu, et al. ∙

∙ 10/29/2020

Accordion: Adaptive Gradient Communication via Critical Learning Regime Identification

Distributed model training suffers from communication bottlenecks due to...

1 Saurabh Agarwal, et al. ∙

∙ 07/09/2020

Attack of the Tails: Yes, You Really Can Backdoor Federated Learning

Due to its decentralized nature, Federated Learning (FL) lends itself to...

18 Hongyi Wang, et al. ∙

∙ 05/27/2019

Scalable K-Medoids via True Error Bound and Familywise Bandits

K-Medoids(KM) is a standard clustering method, used extensively on semi-...

0 Aravindakshan Babu, et al. ∙

∙ 12/05/2018

Graph based Question Answering System

In today's digital age in the dawning era of big data analytics it is no...

0 Piyush Mital, et al. ∙

∙ 11/08/2014

A Novel Approach to Develop a New Hybrid Technique for Trademark Image Retrieval

Trademark Image Retrieval is playing a vital role as a part of CBIR Syst...

0 Saurabh Agarwal, et al. ∙

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