Search Results for author: Jeffrey Dean

Found 13 papers, 9 papers with code

The Deep Learning Revolution and Its Implications for Computer Architecture and Chip Design

no code implementations13 Nov 2019 Jeffrey Dean

The past decade has seen a remarkable series of advances in machine learning, and in particular deep learning approaches based on artificial neural networks, to improve our abilities to build more accurate systems across a broad range of areas, including computer vision, speech recognition, language translation, and natural language understanding tasks.

Natural Language Understanding Speech Recognition +1

Accelerating Deep Learning by Focusing on the Biggest Losers

1 code implementation2 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.

Microscope 2.0: An Augmented Reality Microscope with Real-time Artificial Intelligence Integration

no code implementations21 Nov 2018 Po-Hsuan Cameron Chen, Krishna Gadepalli, Robert MacDonald, Yun Liu, Kunal Nagpal, Timo Kohlberger, Jeffrey Dean, Greg S. Corrado, Jason D. Hipp, Martin C. Stumpe

We demonstrate the utility of ARM in the detection of lymph node metastases in breast cancer and the identification of prostate cancer with a latency that supports real-time workflows.

The Case for Learned Index Structures

6 code implementations4 Dec 2017 Tim Kraska, Alex Beutel, Ed H. Chi, Jeffrey Dean, Neoklis Polyzotis

Indexes are models: a B-Tree-Index can be seen as a model to map a key to the position of a record within a sorted array, a Hash-Index as a model to map a key to a position of a record within an unsorted array, and a BitMap-Index as a model to indicate if a data record exists or not.

Google's Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation

4 code implementations TACL 2017 Melvin Johnson, Mike Schuster, Quoc V. Le, Maxim Krikun, Yonghui Wu, Zhifeng Chen, Nikhil Thorat, Fernanda Viégas, Martin Wattenberg, Greg Corrado, Macduff Hughes, Jeffrey Dean

In addition to improving the translation quality of language pairs that the model was trained with, our models can also learn to perform implicit bridging between language pairs never seen explicitly during training, showing that transfer learning and zero-shot translation is possible for neural translation.

Machine Translation Transfer Learning +1

Zero-Shot Learning by Convex Combination of Semantic Embeddings

2 code implementations19 Dec 2013 Mohammad Norouzi, Tomas Mikolov, Samy Bengio, Yoram Singer, Jonathon Shlens, Andrea Frome, Greg S. Corrado, Jeffrey Dean

In other cases the semantic embedding space is established by an independent natural language processing task, and then the image transformation into that space is learned in a second stage.

Multi-label zero-shot learning

Distributed Representations of Words and Phrases and their Compositionality

34 code implementations NeurIPS 2013 Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg Corrado, Jeffrey Dean

Motivated by this example, we present a simple method for finding phrases in text, and show that learning good vector representations for millions of phrases is possible.

Efficient Estimation of Word Representations in Vector Space

67 code implementations16 Jan 2013 Tomas Mikolov, Kai Chen, Greg Corrado, Jeffrey Dean

We propose two novel model architectures for computing continuous vector representations of words from very large data sets.

Word Similarity

Large Scale Distributed Deep Networks

no code implementations NeurIPS 2012 Jeffrey Dean, Greg Corrado, Rajat Monga, Kai Chen, Matthieu Devin, Mark Mao, Marc'Aurelio Ranzato, Andrew Senior, Paul Tucker, Ke Yang, Quoc V. Le, Andrew Y. Ng

Recent work in unsupervised feature learning and deep learning has shown that being able to train large models can dramatically improve performance.

Object Recognition Speech Recognition

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