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09/08/2023
Meta predictive learning model of natural languages
Large language models based on self-attention mechanisms have achieved a...
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06/20/2023
Eight challenges in developing theory of intelligence
A good theory of mathematical beauty is more practical than any current ...
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05/15/2023
Introduction to dynamical mean-field theory of generic random neural networks
Dynamical mean-field theory is a powerful physics tool used to analyze t...
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12/06/2022
Statistical mechanics of continual learning: variational principle and mean-field potential
An obstacle to artificial general intelligence is set by the continual l...
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08/24/2022
Spectrum of non-Hermitian deep-Hebbian neural networks
Neural networks with recurrent asymmetric couplings are important to und...
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08/21/2022
Emergence of hierarchical modes from deep learning
Large-scale deep neural networks consume expensive training costs, but t...
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11/26/2021
Equivalence between algorithmic instability and transition to replica symmetry breaking in perceptron learning systems
Binary perceptron is a fundamental model of supervised learning for the ...
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02/07/2021
Ensemble perspective for understanding temporal credit assignment
Recurrent neural networks are widely used for modeling spatio-temporal s...
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07/16/2020
Data-driven effective model shows a liquid-like deep learning
Geometric structure of an optimization landscape is argued to be fundame...
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07/04/2020
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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06/20/2020
Weakly-correlated synapses promote dimension reduction in deep neural networks
By controlling synaptic and neural correlations, deep learning has achie...
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06/15/2020
Classification and Recognition of Encrypted EEG Data Neural Network
With the rapid development of Machine Learning technology applied in ele...
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01/10/2020
Learning credit assignment
Deep learning has achieved impressive prediction accuracies in a variety...
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11/11/2019
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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11/06/2019
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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10/19/2019
A Type of Virtual Force based Energy-hole Mitigation Strategy for Sensor Networks
In the era of Big Data and Mobile Internet, how to ensure the terminal d...
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04/30/2019
Minimal model of permutation symmetry in unsupervised learning
Permutation of any two hidden units yields invariant properties in typic...
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10/04/2017
Mean-field 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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05/02/2017
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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03/23/2017
Role of zero synapses in unsupervised feature learning
Synapses in real neural circuits can take discrete values, including zer...
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01/27/2017
Reinforced stochastic gradient descent for deep neural network learning
Stochastic gradient descent (SGD) is a standard optimization method to m...
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12/06/2016
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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02/01/2015