93 citations · 210 across the 6 of their papers we have counts for
8 papers
A Novel Domain Adaptation Framework for Medical Image Segmentation
Amir Gholami, Shashank Subramanian, Varun Shenoy +6
We propose a segmentation framework that uses deep neural networks and introduce two innovations. First, we describe a biophysics-based domain adaptation method. Second, we propose…
SqueezeNext: Hardware-Aware Neural Network Design
Amir Gholami, Kiseok Kwon, Bichen Wu +5
One of the main barriers for deploying neural networks on embedded systems has been large memory and power consumption of existing neural networks. In this work, we introduce Squee…
Shift: A Zero FLOP, Zero Parameter Alternative to Spatial Convolutions
Bichen Wu, Alvin Wan, Xiangyu Yue +6
Neural networks rely on convolutions to aggregate spatial information. However, spatial convolutions are expensive in terms of model size and computation, both of which grow quadra…
Integrated Model, Batch and Domain Parallelism in Training Neural Networks
Amir Gholami, Ariful Azad, Peter Jin +2
We propose a new integrated method of exploiting model, batch and domain parallelism for the training of deep neural networks (DNNs) on large distributed-memory computers using min…
Regret Minimization for Partially Observable Deep Reinforcement Learning
Peter Jin, Kurt Keutzer, Sergey Levine
Deep reinforcement learning algorithms that estimate state and state-action value functions have been shown to be effective in a variety of challenging domains, including learning…
SqueezeDet: Unified, Small, Low Power Fully Convolutional Neural Networks for Real-Time Object Detection for Autonomous Driving
Bichen Wu, Alvin Wan, Forrest Iandola +2
Object detection is a crucial task for autonomous driving. In addition to requiring high accuracy to ensure safety, object detection for autonomous driving also requires real-time…