3 papers
cs.LG2023
Accelerated Neural Network Training with Rooted Logistic Objectives
Zhu Wang, Praveen Raj Veluswami, Harsh Mishra +1
Many neural networks deployed in the real world scenarios are trained using cross entropy based loss functions. From the optimization perspective, it is known that the behavior of…
cs.LG2023
Using Intermediate Forward Iterates for Intermediate Generator Optimization
Harsh Mishra, Jurijs Nazarovs, Manmohan Dogra +1
Score-based models have recently been introduced as a richer framework to model distributions in high dimensions and are generally more suitable for generative tasks. In score-base…
cs.LG2023
Flag Aggregator: Scalable Distributed Training under Failures and Augmented Losses using Convex Optimization
Hamidreza Almasi, Harsh Mishra, Balajee Vamanan +1
Modern ML applications increasingly rely on complex deep learning models and large datasets. There has been an exponential growth in the amount of computation needed to train the l…