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Showing papers by "Jeffrey Dean published in 2019"


Journal ArticleDOI
TL;DR: How these computational techniques can impact a few key areas of medicine and explore how to build end-to-end systems are described.
Abstract: Here we present deep-learning techniques for healthcare, centering our discussion on deep learning in computer vision, natural language processing, reinforcement learning, and generalized methods. We describe how these computational techniques can impact a few key areas of medicine and explore how to build end-to-end systems. Our discussion of computer vision focuses largely on medical imaging, and we describe the application of natural language processing to domains such as electronic health record data. Similarly, reinforcement learning is discussed in the context of robotic-assisted surgery, and generalized deep-learning methods for genomics are reviewed.

1,843 citations


Journal ArticleDOI
TL;DR: This research presents a meta-modelling architecture that automates the very labor-intensive and therefore time-heavy and expensive and therefore expensive and expensive process of manually cataloging and cataloging patient-provider interactions.
Abstract: Machine Learning in Medicine In this view of the future of medicine, patient–provider interactions are informed and supported by massive amounts of data from interactions with similar patients. The...

1,409 citations


Journal ArticleDOI
TL;DR: The augmented reality microscope (ARM) overlays AI-based information onto the current view of the sample in real time, enabling seamless integration of AI into routine workflows and will remove barriers towards the use of AI designed to improve the accuracy and efficiency of cancer diagnosis.
Abstract: The microscopic assessment of tissue samples is instrumental for the diagnosis and staging of cancer, and thus guides therapy. However, these assessments demonstrate considerable variability and many regions of the world lack access to trained pathologists. Though artificial intelligence (AI) promises to improve the access and quality of healthcare, the costs of image digitization in pathology and difficulties in deploying AI solutions remain as barriers to real-world use. Here we propose a cost-effective solution: the augmented reality microscope (ARM). The ARM overlays AI-based information onto the current view of the sample in real time, enabling seamless integration of AI into routine workflows. We demonstrate the utility of ARM in the detection of metastatic breast cancer and the identification of prostate cancer, with latency compatible with real-time use. We anticipate that the ARM will remove barriers towards the use of AI designed to improve the accuracy and efficiency of cancer diagnosis.

174 citations


Posted Content
TL;DR: 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 by reducing the number of computationally-expensive backpropagation steps performed.
Abstract: 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. Selective-Backprop uses the output of a training example's forward pass to decide whether to use that example to compute gradients and update parameters, or to skip immediately to the next example. By reducing the number of computationally-expensive backpropagation steps performed, Selective-Backprop accelerates training. Evaluation on CIFAR10, CIFAR100, and SVHN, across a variety of modern image models, shows that Selective-Backprop converges to target error rates up to 3.5x faster than with standard SGD and between 1.02--1.8x faster than a state-of-the-art importance sampling approach. Further acceleration of 26% can be achieved by using stale forward pass results for selection, thus also skipping forward passes of low priority examples.

59 citations


Posted Content
Jeffrey Dean1
TL;DR: A companion paper to a keynote talk at the 2020 International Solid State Circuits Conference (ISSCC) discussing some of the advances in machine learning, and their implications on the kinds of computational devices we need to build, especially in the post-Moore's Law era.
Abstract: 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. This paper is a companion paper to a keynote talk at the 2020 International Solid-State Circuits Conference (ISSCC) discussing some of the advances in machine learning, and their implications on the kinds of computational devices we need to build, especially in the post-Moore's Law-era. It also discusses some of the ways that machine learning may also be able to help with some aspects of the circuit design process. Finally, it provides a sketch of at least one interesting direction towards much larger-scale multi-task models that are sparsely activated and employ much more dynamic, example- and task-based routing than the machine learning models of today.

48 citations


Journal ArticleDOI
TL;DR: The feasibility of using machine learning to automatically populate a review of systems of all symptoms discussed in an encounter between a patient and a clinician is assessed.
Abstract: This study assesses the feasibility of using machine learning to automatically populate a review of systems of all symptoms discussed in an encounter between a patient and a clinician.

37 citations


Posted Content
TL;DR: It is proposed to foster a new systems machine learning research community at the intersection of the traditional systems and ML communities, focused on topics such as hardware systems for ML, software systems forML, and ML optimized for metrics beyond predictive accuracy.
Abstract: Machine learning (ML) techniques are enjoying rapidly increasing adoption. However, designing and implementing the systems that support ML models in real-world deployments remains a significant obstacle, in large part due to the radically different development and deployment profile of modern ML methods, and the range of practical concerns that come with broader adoption. We propose to foster a new systems machine learning research community at the intersection of the traditional systems and ML communities, focused on topics such as hardware systems for ML, software systems for ML, and ML optimized for metrics beyond predictive accuracy. To do this, we describe a new conference, MLSys, that explicitly targets research at the intersection of systems and machine learning with a program committee split evenly between experts in systems and ML, and an explicit focus on topics at the intersection of the two.

23 citations


01 Jan 2019
TL;DR: Machine Learning in Medicine In as discussed by the authors, a view of the future of medicine, patient-provider interactions are informed and supported by massive amounts of data from interactions with similar patients.
Abstract: Machine Learning in Medicine In this view of the future of medicine, patient–provider interactions are informed and supported by massive amounts of data from interactions with similar patients. The...

11 citations


Proceedings ArticleDOI
Jeffrey Dean1
13 May 2019
TL;DR: Progress in work that research teams have been doing over the past years is described, including advances in difficult problems in artificial intelligence, on building large-scale computer systems for machine learning research, and on applying research and systems to dozens of Google products.
Abstract: In this keynote we describe progress in work that our research teams have been doing over the past years, including advances in difficult problems in artificial intelligence, on building large-scale computer systems for machine learning research, and, in collaboration with many teams at Google, on applying our research and systems to dozens of Google products. Our group has open-sourced the TensorFlow system [2], a widely popular system designed to easily express machine learning ideas, and to quickly train, evaluate and deploy machine learning systems. We then highlight some of our research accomplishments, and relate them to the National Academy of Engineering's Grand Engineering Challenges for the 21st Century. This is joint work with many people at Google.

3 citations


Patent
08 Oct 2019
TL;DR: In this paper, the authors propose a method to improve the quality of the data collected by the data collection system by using the information of the user's interaction with the service provider.
Abstract: 다수의 하드웨어 디바이스에 대한 기계 학습 모델 연산용 배치를 결정하는 방법이 기술된다. 이 방법은 다수의 하드웨어 디바이스상에서 분산 처리를 위해 배치될 기계 학습 모델을 특정하는 데이터를 수신하는 단계와; 데이터로부터, 연산 임베딩의 시퀀스를 생성하는 단계로서, 그 시퀀스내의 각 연산 임베딩은 기계 학습 모델의 처리를 수행하는데 필요한 하나 이상의 개별 연산을 특징화하고; 배치 순환 신경망의 복수의 네트워크 파라미터의 복수의 네트워크 파라미터의 제1 값에 따라 배치 순환 신경망을 사용하여 연산 임베딩 시퀀스를 처리하여, 복수의 디바이스에 대한 시퀀스내의 연산 임베딩에 의해 특징화된 연산들의 배치를 정의하는 네트워크 출력을 생성하는 단계와; 네트워크 출력에 의해 정의된 배치에 따라 다수의 디바이스상에 연산들을 배치함으로써 다수의 하드웨어 디바이스에 의해 처리하기 위한 기계 학습 모델을 스케줄링하는 단계를 포함한다.

1 citations