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Xiangyu Yue

Researcher at University of California, Berkeley

Publications -  59
Citations -  4992

Xiangyu Yue is an academic researcher from University of California, Berkeley. The author has contributed to research in topics: Domain (software engineering) & Computer science. The author has an hindex of 21, co-authored 51 publications receiving 2740 citations. Previous affiliations of Xiangyu Yue include University of California & Stanford University.

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Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Spyridon Bakas, +438 more
- 05 Nov 2018 - 
TL;DR: This study assesses the state-of-the-art machine learning methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018, and investigates the challenge of identifying the best ML algorithms for each of these tasks.
Proceedings ArticleDOI

SqueezeSeg: Convolutional Neural Nets with Recurrent CRF for Real-Time Road-Object Segmentation from 3D LiDAR Point Cloud

Bichen Wu, +3 more
TL;DR: Wu et al. as mentioned in this paper proposed an end-to-end pipeline called SqueezeSeg based on convolutional neural networks (CNN) for semantic segmentation of road-objects from 3D LiDAR point clouds.
Proceedings ArticleDOI

SqueezeSegV2: Improved Model Structure and Unsupervised Domain Adaptation for Road-Object Segmentation from a LiDAR Point Cloud

Bichen Wu, +4 more
TL;DR: Zhou et al. as mentioned in this paper proposed a new model SqueezeSegV2, which is more robust against dropout noises in LiDAR point cloud and therefore achieves significant accuracy improvement.
Proceedings ArticleDOI

Shift: A Zero FLOP, Zero Parameter Alternative to Spatial Convolutions

Bichen Wu, +8 more
TL;DR: ShiftNet as discussed by the authors replaces expensive spatial convolutions with shift-based modules for image classification, face verification, and style transfer, achieving state-of-the-art performance on ImageNet.
Proceedings ArticleDOI

SqueezeNext: Hardware-Aware Neural Network Design

Amir Gholami, +7 more
TL;DR: SqueezeNext as discussed by the authors is a new family of neural network architectures whose design was guided by considering previous architectures such as SqueezeNet, as well as by simulation results on a neural network accelerator.