Search Results for author: Nancy Nayak

Found 6 papers, 0 papers with code

Rotate the ReLU to implicitly sparsify deep networks

no code implementations1 Jun 2022 Nancy Nayak, Sheetal Kalyani

We show that this activation wherein the rotation is learned via training results in the elimination of those parameters/filters in the network which are not important for the task.

Binarized ResNet: Enabling Robust Automatic Modulation Classification at the resource-constrained Edge

no code implementations27 Oct 2021 Deepsayan Sadhukhan, Nitin Priyadarshini Shankar, Nancy Nayak, Thulasi Tholeti, Sheetal Kalyani

The proposed MC method with RBLResNets has an adversarial accuracy of $87. 25\%$ over a wide range of SNRs, surpassing the robustness of all existing SOTA methods to the best of our knowledge.

Adversarial Robustness Binarization

BayesAoA: A Bayesian method for Computation Efficient Angle of Arrival Estimation

no code implementations15 Oct 2021 Akshay Sharma, Nancy Nayak, Sheetal Kalyani

The proposed method achieves $92\%$ accuracy in a channel of noise variance $10^{-6}$ with $19. 3\%$ of the brute-force method's computation.

Understanding Learning Dynamics of Binary Neural Networks via Information Bottleneck

no code implementations13 Jun 2020 Vishnu Raj, Nancy Nayak, Sheetal Kalyani

Compact neural networks are essential for affordable and power efficient deep learning solutions.

Green DetNet: Computation and Memory efficient DetNet using Smart Compression and Training

no code implementations20 Mar 2020 Nancy Nayak, Thulasi Tholeti, Muralikrishnan Srinivasan, Sheetal Kalyani

This paper introduces an incremental training framework for compressing popular Deep Neural Network (DNN) based unfolded multiple-input-multiple-output (MIMO) detection algorithms like DetNet.

Deep Reinforcement Learning based Blind mmWave MIMO Beam Alignment

no code implementations25 Jan 2020 Vishnu Raj, Nancy Nayak, Sheetal Kalyani

Directional beamforming is a crucial component for realizing robust wireless communication systems using millimeter wave (mmWave) technology.

Policy Gradient Methods reinforcement-learning +1

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