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Deep learning

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TLDR
Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years, and will have many more successes in the near future because it requires very little engineering by hand and can easily take advantage of increases in the amount of available computation and data.
Abstract
Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech.

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Citations
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Journal ArticleDOI

Mastering the game of Go with deep neural networks and tree search

TL;DR: Using this search algorithm, the program AlphaGo achieved a 99.8% winning rate against other Go programs, and defeated the human European Go champion by 5 games to 0.5, the first time that a computer program has defeated a human professional player in the full-sized game of Go.
Journal ArticleDOI

A survey on deep learning in medical image analysis

TL;DR: This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year, to survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks.
Journal ArticleDOI

Dermatologist-level classification of skin cancer with deep neural networks

TL;DR: This work demonstrates an artificial intelligence capable of classifying skin cancer with a level of competence comparable to dermatologists, trained end-to-end from images directly, using only pixels and disease labels as inputs.
Journal ArticleDOI

Mastering the game of Go without human knowledge

TL;DR: An algorithm based solely on reinforcement learning is introduced, without human data, guidance or domain knowledge beyond game rules, that achieves superhuman performance, winning 100–0 against the previously published, champion-defeating AlphaGo.
Proceedings ArticleDOI

The Cityscapes Dataset for Semantic Urban Scene Understanding

TL;DR: This work introduces Cityscapes, a benchmark suite and large-scale dataset to train and test approaches for pixel-level and instance-level semantic labeling, and exceeds previous attempts in terms of dataset size, annotation richness, scene variability, and complexity.
References
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Journal ArticleDOI

Deep learning of the tissue-regulated splicing code.

TL;DR: The deep architecture surpasses the performance of the previous Bayesian method for predicting AS patterns and demonstrates that deep architectures can be beneficial, even with a moderately sparse dataset.
Journal ArticleDOI

Convolutional networks can learn to generate affinity graphs for image segmentation

TL;DR: This work presents a machine learning approach to computing an affinity graph using a convolutional network (CN) trained using ground truth provided by human experts and shows that the CN affinity graph can be paired with any standard partitioning algorithm and improves segmentation accuracy significantly compared to standard hand-designed affinity functions.
Journal ArticleDOI

Toward automatic phenotyping of developing embryos from videos

TL;DR: A trainable system for analyzing videos of developing C. elegans embryos that automatically detects, segments, and locates cells and nuclei in microscopic images and contains a set of elastic models of the embryo at various stages of development that are matched to the label images.
Journal ArticleDOI

From machine learning to machine reasoning

TL;DR: Instead of trying to bridge the gap between machine learning systems and sophisticated “all-purpose” inference mechanisms, the set of manipulations applicable to training systems can be algebraically enriched, and reasoning capabilities from the ground up are built.
Journal ArticleDOI

An analog neural network processor with programmable topology

TL;DR: The architecture, implementation, and applications of a special-purpose neural network processor are described and the practicality of the chip is demonstrated with an implementation of a neural network for optical character recognition.
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