6 papers
How to boost autoencoders?
Sai Krishna, Thulasi Tholeti, Sheetal Kalyani
Autoencoders are a category of neural networks with applications in numerous domains and hence, improvement of their performance is gaining substantial interest from the machine le…
On the Differentially Private Nature of Perturbed Gradient Descent
Thulasi Tholeti, Sheetal Kalyani
We consider the problem of empirical risk minimization given a database, using the gradient descent algorithm. We note that the function to be optimized may be non-convex, consisti…
Tune smarter not harder: A principled approach to tuning learning rates for shallow nets
Thulasi Tholeti, Sheetal Kalyani
Effective hyper-parameter tuning is essential to guarantee the performance that neural networks have come to be known for. In this work, a principled approach to choosing the learn…
Green DetNet: Computation and Memory efficient DetNet using Smart Compression and Training
Nancy Nayak, Thulasi Tholeti, Muralikrishnan Srinivasan +1
This paper introduces an incremental training framework for compressing popular Deep Neural Network (DNN) based unfolded multiple-input-multiple-output (MIMO) detection algorithms…
Concavifiability and convergence: necessary and sufficient conditions for gradient descent analysis
Thulasi Tholeti, Sheetal Kalyani
Convergence of the gradient descent algorithm has been attracting renewed interest due to its utility in deep learning applications. Even as multiple variants of gradient descent w…
A Centralized Multi-stage Non-parametric Learning Algorithm for Opportunistic Spectrum Access
Thulasi Tholeti, Vishnu Raj, Sheetal Kalyani
Owing to the ever-increasing demand in wireless spectrum, Cognitive Radio (CR) was introduced as a technique to attain high spectral efficiency. As the number of secondary users (S…