Search Results for author: David G. Andersen

Found 7 papers, 3 papers with code

Learning to Protect Communications with Adversarial Neural Cryptography

9 code implementations21 Oct 2016 Martín Abadi, David G. Andersen

We ask whether neural networks can learn to use secret keys to protect information from other neural networks.

3LC: Lightweight and Effective Traffic Compression for Distributed Machine Learning

no code implementations21 Feb 2018 Hyeontaek Lim, David G. Andersen, Michael Kaminsky

The performance and efficiency of distributed machine learning (ML) depends significantly on how long it takes for nodes to exchange state changes.

BIG-bench Machine Learning Data Compression +1

EDF: Ensemble, Distill, and Fuse for Easy Video Labeling

no code implementations10 Dec 2018 Giulio Zhou, Subramanya Dulloor, David G. Andersen, Michael Kaminsky

We present a way to rapidly bootstrap object detection on unseen videos using minimal human annotations.

Data Augmentation Object +2

Scaling Video Analytics on Constrained Edge Nodes

1 code implementation24 May 2019 Christopher Canel, Thomas Kim, Giulio Zhou, Conglong Li, Hyeontaek Lim, David G. Andersen, Michael Kaminsky, Subramanya R. Dulloor

As video camera deployments continue to grow, the need to process large volumes of real-time data strains wide area network infrastructure.

Computational Efficiency Event Detection

Accelerating Deep Learning by Focusing on the Biggest Losers

2 code implementations2 Oct 2019 Angela H. Jiang, Daniel L. -K. Wong, Giulio Zhou, David G. Andersen, Jeffrey Dean, Gregory R. Ganger, Gauri Joshi, Michael Kaminksy, Michael Kozuch, Zachary C. Lipton, Padmanabhan Pillai

This paper introduces Selective-Backprop, a technique that accelerates the training of deep neural networks (DNNs) by prioritizing examples with high loss at each iteration.

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