16 citations · 20 across the 3 of their papers we have counts for
8 papers
TinyTurbo: Efficient Turbo Decoders on Edge
S Ashwin Hebbar, Rajesh K Mishra, Sravan Kumar Ankireddy +3
In this paper, we introduce a neural-augmented decoder for Turbo codes called TINYTURBO . TINYTURBO has complexity comparable to the classical max-log-MAP algorithm but has much be…
KO codes: Inventing Nonlinear Encoding and Decoding for Reliable Wireless Communication via Deep-learning
Ashok Vardhan Makkuva, Xiyang Liu, Mohammad Vahid Jamali +3
Landmark codes underpin reliable physical layer communication, e.g., Reed-Muller, BCH, Convolution, Turbo, LDPC and Polar codes: each is a linear code and represents a mathematical…
Reed-Muller Subcodes: Machine Learning-Aided Design of Efficient Soft Recursive Decoding
Mohammad Vahid Jamali, Xiyang Liu, Ashok Vardhan Makkuva +3
Reed-Muller (RM) codes are conjectured to achieve the capacity of any binary-input memoryless symmetric (BMS) channel, and are observed to have a comparable performance to that of…
Barracuda: The Power of -polling in Proof-of-Stake Blockchains
Giulia Fanti, Jiantao Jiao, Ashok Makkuva +3
A blockchain is a database of sequential events that is maintained by a distributed group of nodes. A key consensus problem in blockchains is that of determining the next block (da…
Optimal transport mapping via input convex neural networks
Ashok Vardhan Makkuva, Amirhossein Taghvaei, Sewoong Oh +1
In this paper, we present a novel and principled approach to learn the optimal transport between two distributions, from samples. Guided by the optimal transport theory, we learn t…
Learning in Gated Neural Networks
Ashok Vardhan Makkuva, Sewoong Oh, Sreeram Kannan +1
Gating is a key feature in modern neural networks including LSTMs, GRUs and sparsely-gated deep neural networks. The backbone of such gated networks is a mixture-of-experts layer,…