41 citations · 50 across the 8 of their papers we have counts for
13 papers
A Survey on Proactive Customer Care: Enabling Science and Steps to Realize it
Viswanath Ganapathy, Sauptik Dhar, Olimpiya Saha +3
In recent times, advances in artificial intelligence (AI) and IoT have enabled seamless and viable maintenance of appliances in home and building environments. Several studies have…
Stochastic Whitening Batch Normalization
Shengdong Zhang, Ehsan Nezhadarya, Homa Fashandi +3
Batch Normalization (BN) is a popular technique for training Deep Neural Networks (DNNs). BN uses scaling and shifting to normalize activations of mini-batches to accelerate conver…
Stabilizing Bi-Level Hyperparameter Optimization using Moreau-Yosida Regularization
Sauptik Dhar, Unmesh Kurup, Mohak Shah
This research proposes to use the Moreau-Yosida envelope to stabilize the convergence behavior of bi-level Hyperparameter optimization solvers, and introduces the new algorithm cal…
Pruning Algorithms to Accelerate Convolutional Neural Networks for Edge Applications: A Survey
Jiayi Liu, Samarth Tripathi, Unmesh Kurup +1
With the general trend of increasing Convolutional Neural Network (CNN) model sizes, model compression and acceleration techniques have become critical for the deployment of these…
Auptimizer -- an Extensible, Open-Source Framework for Hyperparameter Tuning
Jiayi Liu, Samarth Tripathi, Unmesh Kurup +1
Tuning machine learning models at scale, especially finding the right hyperparameter values, can be difficult and time-consuming. In addition to the computational effort required,…
On-Device Machine Learning: An Algorithms and Learning Theory Perspective
Sauptik Dhar, Junyao Guo, Jiayi Liu +3
The predominant paradigm for using machine learning models on a device is to train a model in the cloud and perform inference using the trained model on the device. However, with i…