Journal ArticleDOI
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.read more
Citations
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Journal ArticleDOI
Deep Learning Applications for Predicting Pharmacological Properties of Drugs and Drug Repurposing Using Transcriptomic Data
Alexander Aliper,Sergey M. Plis,Artem V. Artemov,Alvaro Ulloa,Polina Mamoshina,Alex Zhavoronkov,Alex Zhavoronkov +6 more
TL;DR: This work demonstrates a deep learning neural net trained on transcriptomic data to recognize pharmacological properties of multiple drugs across different biological systems and conditions and proposes using deep neural net confusion matrices for drug repositioning.
Journal ArticleDOI
Artificial Intelligence in Medical Practice: The Question to the Answer?
D. Douglas Miller,Eric W. Brown +1 more
TL;DR: Cognitive programs are impacting medical practice by applying natural language processing to read the rapidly expanding scientific literature and collate years of diverse electronic medical records, suggesting precision therapies for complex illnesses, reduce medical errors, and improve subject enrollment into clinical trials.
Proceedings ArticleDOI
Machine Learning Models that Remember Too Much
TL;DR: A malicious ML provider who supplies model-training code to the data holder, does not observe the training, but then obtains white- or black-box access to the resulting model is considered, to explain how the adversary can extract memorized information from the model.
Journal ArticleDOI
Automated Diagnosis of Plus Disease in Retinopathy of Prematurity Using Deep Convolutional Neural Networks
James M. Brown,J. Peter Campbell,Andrew Beers,Ken Chang,Susan Ostmo,R.V. Paul Chan,Jennifer G. Dy,Deniz Erdogmus,Stratis Ioannidis,Jayashree Kalpathy-Cramer,Jayashree Kalpathy-Cramer,Michael F. Chiang +11 more
TL;DR: This fully automated algorithm diagnosed plus disease in ROP with comparable or better accuracy than human experts, which has potential applications in disease detection, monitoring, and prognosis in infants at risk of ROP.
Journal ArticleDOI
Nucleus segmentation across imaging experiments: the 2018 Data Science Bowl.
Juan C. Caicedo,Allen Goodman,Kyle W. Karhohs,Beth A. Cimini,Jeanelle Ackerman,Marzieh Haghighi,CherKeng Heng,Tim Becker,Minh Doan,Claire McQuin,Mohammad Hossein Rohban,Shantanu Singh,Anne E. Carpenter +12 more
TL;DR: The 2018 Data Science Bowl attracted 3,891 teams worldwide to make the first attempt to build a segmentation method that could be applied to any two-dimensional light microscopy image of stained nuclei across experiments, with no human interaction.
References
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Journal ArticleDOI
Long short-term memory
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Journal ArticleDOI
Gradient-based learning applied to document recognition
Yann LeCun,Léon Bottou,Léon Bottou,Yoshua Bengio,Yoshua Bengio,Yoshua Bengio,Patrick Haffner +6 more
TL;DR: In this article, a graph transformer network (GTN) is proposed for handwritten character recognition, which can be used to synthesize a complex decision surface that can classify high-dimensional patterns, such as handwritten characters.
Journal ArticleDOI
Learning representations by back-propagating errors
TL;DR: Back-propagation repeatedly adjusts the weights of the connections in the network so as to minimize a measure of the difference between the actual output vector of the net and the desired output vector, which helps to represent important features of the task domain.
Journal ArticleDOI
Human-level control through deep reinforcement learning
Volodymyr Mnih,Koray Kavukcuoglu,David Silver,Andrei Rusu,Joel Veness,Marc G. Bellemare,Alex Graves,Martin Riedmiller,Andreas K. Fidjeland,Georg Ostrovski,Stig Petersen,Charles Beattie,Amir Sadik,Ioannis Antonoglou,Helen King,Dharshan Kumaran,Daan Wierstra,Shane Legg,Demis Hassabis +18 more
TL;DR: This work bridges the divide between high-dimensional sensory inputs and actions, resulting in the first artificial agent that is capable of learning to excel at a diverse array of challenging tasks.
Journal ArticleDOI
Reducing the Dimensionality of Data with Neural Networks
TL;DR: In this article, an effective way of initializing the weights that allows deep autoencoder networks to learn low-dimensional codes that work much better than principal components analysis as a tool to reduce the dimensionality of data is described.