Search Results for author: Yihan Jiang

Found 9 papers, 7 papers with code

Bottleneck Analysis of Dynamic Graph Neural Network Inference on CPU and GPU

no code implementations8 Oct 2022 Hanqiu Chen, Yahya Alhinai, Yihan Jiang, Eunjee Na, Cong Hao

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

no code implementations18 Aug 2020 Hyeji Kim, Yihan Jiang, Sreeram Kannan, Sewoong Oh, Pramod Viswanath

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

1 code implementation NeurIPS 2019 Yihan Jiang, Hyeji Kim, Himanshu Asnani, Sreeram Kannan, Sewoong Oh, Pramod Viswanath

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

2 code implementations27 Sep 2019 Yihan Jiang, Jakub Konečný, Keith Rush, Sreeram Kannan

We present FL as a natural source of practical applications for MAML algorithms, and make the following observations.

Federated Learning Meta-Learning

DeepTurbo: Deep Turbo Decoder

1 code implementation6 Mar 2019 Yihan Jiang, Hyeji Kim, Himanshu Asnani, Sreeram Kannan, Sewoong Oh, Pramod Viswanath

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

1 code implementation14 Feb 2019 Shunfu Mao, Yihan Jiang, Edwin Basil Mathew, Sreeram Kannan

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

1 code implementation30 Nov 2018 Yihan Jiang, Hyeji Kim, Himanshu Asnani, Sreeram Kannan, Sewoong Oh, Pramod Viswanath

Designing channel codes under low-latency constraints is one of the most demanding requirements in 5G standards.

Deepcode: Feedback Codes via Deep Learning

1 code implementation NeurIPS 2018 Hyeji Kim, Yihan Jiang, Sreeram Kannan, Sewoong Oh, Pramod Viswanath

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

3 code implementations ICLR 2018 Hyeji Kim, Yihan Jiang, Ranvir Rana, Sreeram Kannan, Sewoong Oh, Pramod Viswanath

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).

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