8 citations · 17 across the 6 of their papers we have counts for
6 papers
OR Else: A Differentiable Trust Region for Policy Optimization
Chinmay Rane, Kanishka Tyagi, Michael Manry
PPO and the GRPO baseline studied here use clipped surrogate objectives whose favorable-direction saturation introduces an abrupt change in the scalar objective's derivative. We as…
ConvFormer3D-TAP: Phase/Uncertainty-Aware Front-End Fusion for Cine CMR View Classification Pipelines
Nafiseh Ghaffar Nia, Vinesh Appadurai, Suchithra V. +4
Reliable recognition of standard cine cardiac MRI views is essential because each view determines which cardiac anatomy is visualized and which quantitative analyses can be perform…
Making Sigmoid-MSE Great Again: Output Reset Challenges Softmax Cross-Entropy in Neural Network Classification
Kanishka Tyagi, Chinmay Rane, Ketaki Vaidya +3
This study presents a comparative analysis of two objective functions, Mean Squared Error (MSE) and Softmax Cross-Entropy (SCE) for neural network classification tasks. While SCE c…
Automated Sizing and Training of Efficient Deep Autoencoders using Second Order Algorithms
Kanishka Tyagi, Chinmay Rane, Michael Manry
We propose a multi-step training method for designing generalized linear classifiers. First, an initial multi-class linear classifier is found through regression. Then validation e…
Optimizing Performance of Feedforward and Convolutional Neural Networks through Dynamic Activation Functions
Chinmay Rane, Kanishka Tyagi, Michael Manry
Deep learning training training algorithms are a huge success in recent years in many fields including speech, text,image video etc. Deeper and deeper layers are proposed with huge…
Optimal Input Gain: All You Need to Supercharge a Feed-Forward Neural Network
Chinmay Rane, Kanishka Tyagi, Sanjeev Malalur +2
Linear transformation of the inputs alters the training performance of feed-forward networks that are otherwise equivalent. However, most linear transforms are viewed as a pre-proc…