most citedLinSBFT: Linear-Communication One-Step BFT Protocol for Public Blockchains

3 citations · 7 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CV20211 cited

YOLO-ReT: Towards High Accuracy Real-time Object Detection on Edge GPUs

Prakhar Ganesh, Yao Chen, Yin Yang +2

Performance of object detection models has been growing rapidly on two major fronts, model accuracy and efficiency. However, in order to map deep neural network (DNN) based object…

cs.CV2021

Unsupervised Image Generation with Infinite Generative Adversarial Networks

Hui Ying, He Wang, Tianjia Shao +2

Image generation has been heavily investigated in computer vision, where one core research challenge is to generate images from arbitrarily complex distributions with little superv…

cs.CR20211 cited

Training Massive Deep Neural Networks in a Smart Contract: A New Hope

Yin Yang

Deep neural networks (DNNs) could be very useful in blockchain applications such as DeFi and NFT trading. However, training / running large-scale DNNs as part of a smart contract i…

cs.CV2021

In-game Residential Home Planning via Visual Context-aware Global Relation Learning

Lijuan Liu, Yin Yang, Yi Yuan +3

In this paper, we propose an effective global relation learning algorithm to recommend an appropriate location of a building unit for in-game customization of residential home comp…

cs.LG20202 cited

Second-order Neural Network Training Using Complex-step Directional Derivative

Siyuan Shen, Tianjia Shao, Kun Zhou +3

While the superior performance of second-order optimization methods such as Newton's method is well known, they are hardly used in practice for deep learning because neither assemb…

cs.DC20203 cited

LinSBFT: Linear-Communication One-Step BFT Protocol for Public Blockchains

Xiaodong Qi, Yin Yang, Zhao Zhang +2

This paper presents LinSBFT, a Byzantine Fault Tolerance (BFT) protocol with the capacity of processing over 2000 smart contract transactions per second in production. LinSBFT appl…