4 citations · 9 across the 6 of their papers we have counts for
6 papers
SpeedLimit: Neural Architecture Search for Quantized Transformer Models
Yuji Chai, Luke Bailey, Yunho Jin +5
While research in the field of transformer models has primarily focused on enhancing performance metrics such as accuracy and perplexity, practical applications in industry often n…
SphereFed: Hyperspherical Federated Learning
Xin Dong, Sai Qian Zhang, Ang Li +1
Federated Learning aims at training a global model from multiple decentralized devices (i.e. clients) without exchanging their private local data. A key challenge is the handling o…
SplitNets: Designing Neural Architectures for Efficient Distributed Computing on Head-Mounted Systems
Xin Dong, Barbara De Salvo, Meng Li +4
We design deep neural networks (DNNs) and corresponding networks' splittings to distribute DNNs' workload to camera sensors and a centralized aggregator on head mounted devices to…
FAST: DNN Training Under Variable Precision Block Floating Point with Stochastic Rounding
Sai Qian Zhang, Bradley McDanel, H. T. Kung
Block Floating Point (BFP) can efficiently support quantization for Deep Neural Network (DNN) training by providing a wide dynamic range via a shared exponent across a group of val…
Privacy Vulnerability of Split Computing to Data-Free Model Inversion Attacks
Xin Dong, Hongxu Yin, Jose M. Alvarez +3
Mobile edge devices see increased demands in deep neural networks (DNNs) inference while suffering from stringent constraints in computing resources. Split computing (SC) emerges a…
Neural Mean Discrepancy for Efficient Out-of-Distribution Detection
Xin Dong, Junfeng Guo, Ang Li +3
Various approaches have been proposed for out-of-distribution (OOD) detection by augmenting models, input examples, training sets, and optimization objectives. Deviating from exist…