661 citations · 701 across the 9 of their papers we have counts for
9 papers
Nitrogen-Doped TiCT Coated with a Molecularly Imprinted Polymer as Efficient Cathode Material for Lithium-Sulfur Batteries
Feng Yan, Liqiang Lu, Chongan Ye +12
Due to their high energy density (2600 Wh/kg), low cost, and low environmental impact, lithium-sulfur batteries are considered a promising alternative to lithium-ion batteries. How…
MgH2 nanoparticles confined in reduced graphene oxide pillared with organosilica: a novel type of hydrogen storage material
Feng Yan, Estela Moreton Alfonsín, Peter Ngene +11
Hydrogen is a promising energy carrier that can push forward the energy transition because of its high energy density (142 MJ kg-1), variety of potential sources, low weight and lo…
InfiniStore: Elastic Serverless Cloud Storage
Jingyuan Zhang, Ao Wang, Xiaolong Ma +8
Cloud object storage such as AWS S3 is cost-effective and highly elastic but relatively slow, while high-performance cloud storage such as AWS ElastiCache is expensive and provides…
AI Augmented Edge and Fog Computing: Trends and Challenges
Shreshth Tuli, Fatemeh Mirhakimi, Samodha Pallewatta +6
In recent years, the landscape of computing paradigms has witnessed a gradual yet remarkable shift from monolithic computing to distributed and decentralized paradigms such as Inte…
BiFeat: Supercharge GNN Training via Graph Feature Quantization
Yuxin Ma, Ping Gong, Jun Yi +4
Graph Neural Networks (GNNs) is a promising approach for applications with nonEuclidean data. However, training GNNs on large scale graphs with hundreds of millions nodes is both r…
EPNAS: Efficient Progressive Neural Architecture Search
Yanqi Zhou, Peng Wang, Sercan Arik +4
In this paper, we propose Efficient Progressive Neural Architecture Search (EPNAS), a neural architecture search (NAS) that efficiently handles large search space through a novel p…
LEASGD: an Efficient and Privacy-Preserving Decentralized Algorithm for Distributed Learning
Hsin-Pai Cheng, Patrick Yu, Haojing Hu +4
Distributed learning systems have enabled training large-scale models over large amount of data in significantly shorter time. In this paper, we focus on decentralized distributed…
Differentiable Fine-grained Quantization for Deep Neural Network Compression
Hsin-Pai Cheng, Yuanjun Huang, Xuyang Guo +4
Neural networks have shown great performance in cognitive tasks. When deploying network models on mobile devices with limited resources, weight quantization has been widely adopted…
TernGrad: Ternary Gradients to Reduce Communication in Distributed Deep Learning
Wei Wen, Cong Xu, Feng Yan +4
High network communication cost for synchronizing gradients and parameters is the well-known bottleneck of distributed training. In this work, we propose TernGrad that uses ternary…