no code implementations • 16 Mar 2024 • Sravan Kumar Ankireddy, Krishna Narayanan, Hyeji Kim
First, we demonstrate that POWERBLAST, an analytical coding scheme inspired by Schalkwijk-Kailath (SK) and Gallager-Nakiboglu (GN) schemes, achieves notable reliability improvements over both SK and GN schemes, outperforming neural codes in high signal-to-noise ratio (SNR) regions.
no code implementations • 1 Nov 2023 • Vicram Rajagopalan, Vishnu Teja Kunde, Chandra Shekhara Kaushik Valmeekam, Krishna Narayanan, Srinivas Shakkottai, Dileep Kalathil, Jean-Francois Chamberland
A communication channel is essentially a noisy function that maps transmitted symbols to received symbols, and this function can be represented by an unknown parameter whose statistics depend on an (also unknown) latent context.
2 code implementations • 6 Jun 2023 • Chandra Shekhara Kaushik Valmeekam, Krishna Narayanan, Dileep Kalathil, Jean-Francois Chamberland, Srinivas Shakkottai
We provide new estimates of an asymptotic upper bound on the entropy of English using the large language model LLaMA-7B as a predictor for the next token given a window of past tokens.
no code implementations • 15 Dec 2021 • Arman Hasanzadeh, Mohammadreza Armandpour, Ehsan Hajiramezanali, Mingyuan Zhou, Nick Duffield, Krishna Narayanan
By learning distributional representations, we provide uncertainty estimates in downstream graph analytics tasks and increase the expressive power of the predictive model.
1 code implementation • ICML 2020 • Arman Hasanzadeh, Ehsan Hajiramezanali, Shahin Boluki, Mingyuan Zhou, Nick Duffield, Krishna Narayanan, Xiaoning Qian
We propose a unified framework for adaptive connection sampling in graph neural networks (GNNs) that generalizes existing stochastic regularization methods for training GNNs.
no code implementations • 28 Oct 2019 • Ehsan Hajiramezanali, Arman Hasanzadeh, Nick Duffield, Krishna Narayanan, Mingyuan Zhou, Xiaoning Qian
Stochastic recurrent neural networks with latent random variables of complex dependency structures have shown to be more successful in modeling sequential data than deterministic deep models.