4 citations · 4 across the 6 of their papers we have counts for
6 papers
Held-out evidence resolves follow-up measurement decisions in biological screens
Jia Bi, Samuel Pinilla, Chenyang Zhu
Machine learning determines which follow-up measurements biological screens collect. In a six-rule Cell Painting battery, the highest-value rule would re-image 96.01% of the librar…
GhostShiftAddNet: More Features from Energy-Efficient Operations
Jia Bi, Jonathon Hare, Geoff V. Merrett
Deep convolutional neural networks (CNNs) are computationally and memory intensive. In CNNs, intensive multiplication can have resource implications that may challenge the ability…
Dynamic Transformer for Efficient Machine Translation on Embedded Devices
Hishan Parry, Lei Xun, Amin Sabet +3
The Transformer architecture is widely used for machine translation tasks. However, its resource-intensive nature makes it challenging to implement on constrained embedded devices,…
Dynamic-OFA: Runtime DNN Architecture Switching for Performance Scaling on Heterogeneous Embedded Platforms
Wei Lou, Lei Xun, Amin Sabet +3
Mobile and embedded platforms are increasingly required to efficiently execute computationally demanding DNNs across heterogeneous processing elements. At runtime, the available ha…
A Variance Controlled Stochastic Method with Biased Estimation for Faster Non-convex Optimization
Jia Bi, Steve R. Gunn
In this paper, we proposed a new technique, {\em variance controlled stochastic gradient} (VCSG), to improve the performance of the stochastic variance reduced gradient (SVRG) algo…
A Stochastic Gradient Method with Biased Estimation for Faster Nonconvex Optimization
Jia Bi, Steve R. Gunn
A number of optimization approaches have been proposed for optimizing nonconvex objectives (e.g. deep learning models), such as batch gradient descent, stochastic gradient descent…