Journal ArticleDOI
Deep learning
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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Proceedings ArticleDOI
Deep Learning for Entity Matching: A Design Space Exploration
Sidharth Mudgal,Han Li,Theodoros Rekatsinas,AnHai Doan,Youngchoon Park,Ganesh Krishnan,Rohit Deep,Esteban Arcaute,Vijay Raghavendra +8 more
TL;DR: The results show that DL does not outperform current solutions on structured EM, but it can significantly outperform them on textual and dirty EM, which suggests that practitioners should seriously consider using DL for textual anddirty EM problems.
Journal ArticleDOI
Modeling and forecasting building energy consumption: A review of data-driven techniques
TL;DR: A review of studies developing data-driven models for building scale applications with a focus on the input data characteristics and data pre-processing methods, the building typologies considered, the targeted energy end-uses and forecasting horizons, and accuracy assessment.
Posted Content
Adversarial Examples: Attacks and Defenses for Deep Learning
TL;DR: In this paper, the authors present a taxonomy of methods for generating adversarial examples for deep neural networks and further elaborate on countermeasures for adversarial example and explore the challenges and the potential solutions.
Journal ArticleDOI
druGAN: An Advanced Generative Adversarial Autoencoder Model for de Novo Generation of New Molecules with Desired Molecular Properties in Silico
Artur Kadurin,Sergey I. Nikolenko,Kuzma Khrabrov,Alexander Aliper,Alex Zhavoronkov,Alex Zhavoronkov +5 more
TL;DR: This work developed an advanced AAE model for molecular feature extraction problems, and demonstrated its advantages compared to VAE in terms of adjustability in generating molecular fingerprints; capacity of processing very large molecular data sets; and efficiency in unsupervised pretraining for regression model.
Journal ArticleDOI
Deep Learning Models for Wireless Signal Classification With Distributed Low-Cost Spectrum Sensors
TL;DR: In this article, a new data-driven model for automatic modulation classification based on long short term memory (LSTM) is proposed, which learns from the time domain amplitude and phase information of the modulation schemes present in the training data without requiring expert features like higher order cyclic moments.
References
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Long short-term memory
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Gradient-based learning applied to document recognition
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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.
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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.