Human-level control through deep reinforcement learning - Wikidata - thesis-toulmin - TriplyDB

Human-level control through deep reinforcement learning

Human-level control through deep reinforcement learning

http://www.wikidata.org/entity/Q27907579

about

Comparison of deep neural networks to spatio-temporal cortical dynamics of human visual object recognition reveals hierarchical correspondence Deep learning The metabolome 18 years on: a concept comes of age Predicting Virtual World User Population Fluctuations with Deep Learning Toward an Integration of Deep Learning and Neuroscience The major synthetic evolutionary transitions Interactive machine learning for health informatics: when do we need the human-in-the-loop?Atoms, bits, and cells Hybrid computing using a neural network with dynamic external memory Neuroblastoma, a Paradigm for Big Data Science in Pediatric Oncology.Building Machines That Learn and Think Like People The Role of Architectural and Learning Constraints in Neural Network Models: A Case Study on Visual Space Coding.An Adaptive Multi-Sensor Data Fusion Method Based on Deep Convolutional Neural Networks for Fault Diagnosis of Planetary Gearbox Deep Learning with Convolutional Neural Networks Applied to Electromyography Data: A Resource for the Classification of Movements for Prosthetic Hands.Learning to learn about uncertain feedback Neural Networks for the Prediction of Organic Chemistry Reactions.Generating highly accurate prediction hypotheses through collaborative ensemble learning.Hybrid Network Model for "Deep Learning" of Chemical Data: Application to Antimicrobial Peptides.Face classification using electronic synapses Understanding the dynamics of terrorism events with multiple-discipline datasets and machine learning approach Event-Driven Random Back-Propagation: Enabling Neuromorphic Deep Learning Machines Human-level concept learning through probabilistic program induction.Artificial intelligence: Learning to see and act.Implicit Value Updating Explains Transitive Inference Performance: The Betasort Model.High-Content Analysis of Breast Cancer Using Single-Cell Deep Transfer Learning.Autonomous Optimization of Targeted Stimulation of Neuronal Networks Curiosity Search: Producing Generalists by Encouraging Individuals to Continually Explore and Acquire Skills throughout Their Lifetime.Could a Neuroscientist Understand a Microprocessor?Multiagent cooperation and competition with deep reinforcement learning A state space approach for piecewise-linear recurrent neural networks for identifying computational dynamics from neural measurements.Goal-Directed Decision Making with Spiking Neurons.Fast Sequences of Non-spatial State Representations in Humans.Efficient Actor-Critic Algorithm with Hierarchical Model Learning and Planning.Automated analysis of retinal imaging using machine learning techniques for computer vision.Prediction error, ketamine and psychosis: An updated model.Mobile robots exploration through cnn-based reinforcement learning Market Model for Resource Allocation in Emerging Sensor Networks with Reinforcement Learning.Behaviour Centred Design: towards an applied science of behaviour change.Supervised Machine Learning Algorithms Can Classify Open-Text Feedback of Doctor Performance With Human-Level Accuracy Overcoming catastrophic forgetting in neural networks.

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Human-level control through deep reinforcement learning

http://www.wikidata.org/entity/Q27907579

description

2015 nî lūn-bûn

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2015 թուականի Փետրուարին հրատարակուած գիտական յօդուած

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2015 թվականի փետրվարին հրատարակված գիտական հոդված

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2015年の論文

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2015年論文

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2015年論文

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2015年論文

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2015年論文

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2015年論文

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2015年论文

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name

Human-level control through deep reinforcement learning

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Human-level control through deep reinforcement learning

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Human-level control through deep reinforcement learning

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Human-level control through deep reinforcement learning

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Human-level control through deep reinforcement learning

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Human-level control through deep reinforcement learning

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Human-level control through deep reinforcement learning

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Human-level control through deep reinforcement learning

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Human-level control through deep reinforcement learning

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Human-level control through deep reinforcement learning

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Human-level control through deep reinforcement learning

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Human-level control through deep reinforcement learning

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Human-level control through deep reinforcement learning

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Human-level control through deep reinforcement learning

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Human-level control through deep reinforcement learning

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Human-level control through deep reinforcement learning

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Human-level control through deep reinforcement learning

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Human-level control through deep reinforcement learning

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Human-level control through deep reinforcement learning

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Human-level control through deep reinforcement learning

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Human-level control through deep reinforcement learning

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Human-level control through deep reinforcement learning

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Human-level control through deep reinforcement learning

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Human-level control through deep reinforcement learning

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Human-level control through deep reinforcement learning

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Alex Graves

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Amir Sadik

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Andreas K. Fidjeland

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Andrei A. Rusu

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Charles Beattie

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Daan Wierstra

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Georg Ostrovski

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Helen King

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Ioannis Antonoglou

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Joel Veness

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529-533

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scholarly article

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642231

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10.1038/NATURE14236

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7540

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518

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Koray Kavukcuoglu

Dharshan Kumaran

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2015-02-25T00:00:00Z

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1030517994

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25719670

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reinforcement learning