Search Results for author:
Found 9 papers, 7 papers with code
Bottleneck Analysis of Dynamic Graph Neural Network Inference on CPU and GPU
A variety of dynamic graph neural networks designed from algorithmic perspectives have succeeded in incorporating temporal information into graph processing.
Deepcode and Modulo-SK are Designed for Different Settings
DeepCode is designed and evaluated for the AWGN channel with (potentially delayed) uncoded output feedback.
Turbo Autoencoder: Deep learning based channel codes for point-to-point communication channels
Designing codes that combat the noise in a communication medium has remained a significant area of research in information theory as well as wireless communications.
Improving Federated Learning Personalization via Model Agnostic Meta Learning
We present FL as a natural source of practical applications for MAML algorithms, and make the following observations.
DeepTurbo: Deep Turbo Decoder
We focus on Turbo codes and propose DeepTurbo, a novel deep learning based architecture for Turbo decoding.
BOAssembler: a Bayesian Optimization Framework to Improve RNA-Seq Assembly Performance
High throughput sequencing of RNA (RNA-Seq) can provide us with millions of short fragments of RNA transcripts from a sample.
LEARN Codes: Inventing Low-latency Codes via Recurrent Neural Networks
Designing channel codes under low-latency constraints is one of the most demanding requirements in 5G standards.
Deepcode: Feedback Codes via Deep Learning
The design of codes for communicating reliably over a statistically well defined channel is an important endeavor involving deep mathematical research and wide-ranging practical applications.
Communication Algorithms via Deep Learning
We show that creatively designed and trained RNN architectures can decode well known sequential codes such as the convolutional and turbo codes with close to optimal performance on the additive white Gaussian noise (AWGN) channel, which itself is achieved by breakthrough algorithms of our times (Viterbi and BCJR decoders, representing dynamic programing and forward-backward algorithms).
