Shashank Jere | DeepAI

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∙ 08/04/2023

Universal Approximation of Linear Time-Invariant (LTI) Systems through RNNs: Power of Randomness in Reservoir Computing

Recurrent neural networks (RNNs) are known to be universal approximators...

0 Shashank Jere, et al. ∙

∙ 03/02/2023

Distributed Learning Meets 6G: A Communication and Computing Perspective

With the ever-improving computing capabilities and storage capacities of...

0 Shashank Jere, et al. ∙

∙ 06/28/2021

Federated Dynamic Spectrum Access

Due to the growing volume of data traffic produced by the surge of Inter...

0 Yifei Song, et al. ∙

∙ 12/01/2020

Learning with Knowledge of Structure: A Neural Network-Based Approach for MIMO-OFDM Detection

In this paper, we explore neural network-based strategies for performing...

0 Zhou Zhou, et al. ∙

∙ 11/10/2020

Learning for Integer-Constrained Optimization through Neural Networks with Limited Training

In this paper, we investigate a neural network-based learning approach t...

0 Zhou Zhou, et al. ∙

∙ 07/15/2020

Federated Learning in Mobile Edge Computing: An Edge-Learning Perspective for Beyond 5G

Owing to the large volume of sensed data from the enormous number of IoT...

0 Shashank Jere, et al. ∙

∙ 03/15/2020

RCNet: Incorporating Structural Information into Deep RNN for MIMO-OFDM Symbol Detection with Limited Training

In this paper, we investigate learning-based MIMO-OFDM symbol detection ...

0 Zhou Zhou, et al. ∙

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