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Kurt Keutzer

Researcher at University of California, Berkeley

Publications -  439
Citations -  26512

Kurt Keutzer is an academic researcher from University of California, Berkeley. The author has contributed to research in topics: Computer science & Artificial neural network. The author has an hindex of 75, co-authored 412 publications receiving 20525 citations. Previous affiliations of Kurt Keutzer include Massachusetts Institute of Technology & Bell Labs.

Papers
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Proceedings ArticleDOI

FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search

Bichen Wu, +9 more
TL;DR: This work proposes a differentiable neural architecture search (DNAS) framework that uses gradient-based methods to optimize ConvNet architectures, avoiding enumerating and training individual architectures separately as in previous methods.
Posted ContentDOI

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.
Posted Content

DenseNet: Implementing Efficient ConvNet Descriptor Pyramids

Forrest Iandola, +5 more
- 07 Apr 2014 - 
TL;DR: DenseNet is presented, an open source system that computes dense, multiscale features from the convolutional layers of a CNN based object classifier.
Book ChapterDOI

Dense point trajectories by GPU-accelerated large displacement optical flow

Narayanan Sundaram, +2 more
TL;DR: This paper provides a method for computing point trajectories based on a fast parallel implementation of a recent optical flow algorithm that tolerates fast motion and proves that the fixed point matrix obtained in the optical flow technique is positive semi-definite.