48 citations · 113 across the 9 of their papers we have counts for
10 papers
No Shifted Augmentations (NSA): compact distributions for robust self-supervised Anomaly Detection
Mohamed Yousef, Marcel Ackermann, Unmesh Kurup +1
Unsupervised Anomaly detection (AD) requires building a notion of normalcy, distinguishing in-distribution (ID) and out-of-distribution (OOD) data, using only available ID samples.…
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…