
Ensemble perspective for understanding temporal credit assignment
Recurrent neural networks are widely used for modeling spatiotemporal s...
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Datadriven effective model shows a liquidlike deep learning
Geometric structure of an optimization landscape is argued to be fundame...
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Relationship between manifold smoothness and adversarial vulnerability in deep learning with local errors
Artificial neural networks can achieve impressive performances, and even...
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Weaklycorrelated synapses promote dimension reduction in deep neural networks
By controlling synaptic and neural correlations, deep learning has achie...
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Classification and Recognition of Encrypted EEG Data Neural Network
With the rapid development of Machine Learning technology applied in ele...
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Learning credit assignment
Deep learning has achieved impressive prediction accuracies in a variety...
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How data, synapses and neurons interact with each other: a variational principle marrying gradient ascent and message passing
Unsupervised learning requiring only raw data is not only a fundamental ...
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Statistical physics of unsupervised learning with prior knowledge in neural networks
Integrating sensory inputs with prior beliefs from past experiences in u...
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A Type of Virtual Force based Energyhole Mitigation Strategy for Sensor Networks
In the era of Big Data and Mobile Internet, how to ensure the terminal d...
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Minimal model of permutation symmetry in unsupervised learning
Permutation of any two hidden units yields invariant properties in typic...
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Meanfield theory of input dimensionality reduction in unsupervised deep neural networks
Deep neural networks as powerful tools are widely used in various domain...
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Redundancy in active paths of deep networks: a random active path model
Deep learning has become a powerful and popular tool for a variety of ma...
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Role of zero synapses in unsupervised feature learning
Synapses in real neural circuits can take discrete values, including zer...
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Reinforced stochastic gradient descent for deep neural network learning
Stochastic gradient descent (SGD) is a standard optimization method to m...
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Statistical mechanics of unsupervised feature learning in a restricted Boltzmann machine with binary synapses
Revealing hidden features in unlabeled data is called unsupervised featu...
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Advanced Mean Field Theory of Restricted Boltzmann Machine
Learning in restricted Boltzmann machine is typically hard due to the co...
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Haiping Huang
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