86 citations · 209 across the 19 of their papers we have counts for
20 papers
A Social Outcomes and Priorities centered (SOP) Framework for AI policy
Mohak Shah
Rapid developments in AI and its adoption across various domains have necessitated a need to build robust guardrails and risk containment plans while ensuring equitable benefits fo…
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…
Universum GANs: Improving GANs through contradictions
Sauptik Dhar, Javad Heydari, Samarth Tripathi +2
Limited availability of labeled-data makes any supervised learning problem challenging. Alternative learning settings like semi-supervised and universum learning alleviate the depe…
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…
Variable Metric Proximal Gradient Method with Diagonal Barzilai-Borwein Stepsize
Youngsuk Park, Sauptik Dhar, Stephen Boyd +1
Variable metric proximal gradient (VM-PG) is a widely used class of convex optimization method. Lately, there has been a lot of research on the theoretical guarantees of VM-PG with…
Improving Model Training by Periodic Sampling over Weight Distributions
Samarth Tripathi, Jiayi Liu, Unmesh Kurup +2
In this paper, we explore techniques centered around periodic sampling of model weights that provide convergence improvements on gradient update methods (vanilla \acs{SGD}, Momentu…
Is it Safe to Drive? An Overview of Factors, Challenges, and Datasets for Driveability Assessment in Autonomous Driving
Junyao Guo, Unmesh Kurup, Mohak Shah
With recent advances in learning algorithms and hardware development, autonomous cars have shown promise when operating in structured environments under good driving conditions. Ho…
Multiclass Universum SVM
Sauptik Dhar, Vladimir Cherkassky, Mohak Shah
We introduce Universum learning for multiclass problems and propose a novel formulation for multiclass universum SVM (MU-SVM). We also propose an analytic span bound for model sele…
Make (Nearly) Every Neural Network Better: Generating Neural Network Ensembles by Weight Parameter Resampling
Jiayi Liu, Samarth Tripathi, Unmesh Kurup +1
Deep Neural Networks (DNNs) have become increasingly popular in computer vision, natural language processing, and other areas. However, training and fine-tuning a deep learning mod…
Effective Building Block Design for Deep Convolutional Neural Networks using Search
Jayanta K Dutta, Jiayi Liu, Unmesh Kurup +1
Deep learning has shown promising results on many machine learning tasks but DL models are often complex networks with large number of neurons and layers, and recently, complex lay…
Concept Drift Detection and Adaptation with Hierarchical Hypothesis Testing
Shujian Yu, Zubin Abraham, Heng Wang +3
A fundamental issue for statistical classification models in a streaming environment is that the joint distribution between predictor and response variables changes over time (a ph…
Deep Symbolic Representation Learning for Heterogeneous Time-series Classification
Shengdong Zhang, Soheil Bahrampour, Naveen Ramakrishnan +1
In this paper, we consider the problem of event classification with multi-variate time series data consisting of heterogeneous (continuous and categorical) variables. The complex t…
Universum Learning for Multiclass SVM
Sauptik Dhar, Naveen Ramakrishnan, Vladimir Cherkassky +1
We introduce Universum learning for multiclass problems and propose a novel formulation for multiclass universum SVM (MU-SVM). We also propose a span bound for MU-SVM that can be u…
Comparative Study of Deep Learning Software Frameworks
Soheil Bahrampour, Naveen Ramakrishnan, Lukas Schott +1
Deep learning methods have resulted in significant performance improvements in several application domains and as such several software frameworks have been developed to facilitate…
Big Data and the Internet of Things
Mohak Shah
Advances in sensing and computing capabilities are making it possible to embed increasing computing power in small devices. This has enabled the sensing devices not just to passive…
Feature Selection with Conjunctions of Decision Stumps and Learning from Microarray Data
Mohak Shah, Mario Marchand, Jacques Corbeil
One of the objectives of designing feature selection learning algorithms is to obtain classifiers that depend on a small number of attributes and have verifiable future performance…