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Deep Learning in Neural Networks: An Overview
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In recent years, deep artificial neural networks (including recurrent ones) have won numerous contests in pattern recognition and machine learning. This historical survey compactly summarises relevant work, much of it from the previous millennium. Shallow and deep learners are distinguished by the depth of their credit assignment paths, which are chains of possibly learnable, causal links between actions and effects. I review deep supervised learning (also recapitulating the history of backpropagation), unsupervised learning, reinforcement learning & evolutionary computation, and indirect search for short programs encoding deep and large networks.
Forward citations
Cited by 7 Pith papers
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R-process heating implementation in hydrodynamic simulations with neural networks
RHINE emulates r-process heating in NSM hydro simulations via neural networks trained on full nuclear trajectories, achieving <10% agreement with post-processing and boosting BH-torus ejecta mass by 40%.
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Efficient dataset construction using active learning and uncertainty-aware neural networks for plasma turbulent transport surrogate models
Active learning with SNGP and BNN-NCP models constructs QuaLiKiz surrogate training sets from 100 to 10,000 points, beating random sampling and approaching prior ensemble-based ADEPT efficiency.
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Reproducibility of machine learning analyses of 21 cm reionization maps
Convolutional networks trained on 21 cm reionization images often memorize simulation boxes rather than physics, yielding high same-box test scores but poor performance on unseen simulations.
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Reduced Order Models and Conditional Expectation -- Analysing Parametric Low-Order Approximations
Parametric reduced-order models built by least-squares projection, including POD, reduced basis methods, and Gaussian process emulation, can be viewed as conditional expectations in a Bayesian updating framework.
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A Novel Privacy-Preserving Deep Learning Scheme without Using Cryptography Component
A split neural network with a secret output-flip signal is claimed to protect input, output, and model privacy without cryptography, but the security argument conflates non-uniqueness with privacy and ignores known in...
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Attentive Deep Regression Networks for Real-Time Visual Face Tracking in Video Surveillance
An attentive GOTURN extension using VGG-Face features scores 0.807 overall on ChokePoint face tracking, compared with 0.506 for GOTURN and 0.348 for IVT.
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Implementing An Artificial Quantum Perceptron
A Qiskit quantum perceptron can recover a hand-picked weight on a synthetic dataset, but the claimed exponential advantage is not demonstrated.
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