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Found 15 papers, 1 papers with code
The star-shaped space of solutions of the spherical negative perceptron
Empirical studies on the landscape of neural networks have shown that low-energy configurations are often found in complex connected structures, where zero-energy paths between pairs of distant solutions can be constructed.
Storage and Learning phase transitions in the Random-Features Hopfield Model
The Hopfield model is a paradigmatic model of neural networks that has been analyzed for many decades in the statistical physics, neuroscience, and machine learning communities.
Noise-cleaning the precision matrix of fMRI time series
We observe that the so called Optimal Rotationally Invariant Estimator, based on Random Matrix Theory, leads to a significantly lower distance from the true precision matrix in synthetic data, and higher test likelihood in natural fMRI data.
Information-theoretical analysis of the neural code for decoupled face representation
Processing faces accurately and efficiently is a key capability of humans and other animals that engage in sophisticated social tasks.
Deep learning via message passing algorithms based on belief propagation
Message-passing algorithms based on the Belief Propagation (BP) equations constitute a well-known distributed computational scheme.
Reconstruction of Pairwise Interactions using Energy-Based Models
Pairwise models like the Ising model or the generalized Potts model have found many successful applications in fields like physics, biology, and economics.
Entropic gradient descent algorithms and wide flat minima
The properties of flat minima in the empirical risk landscape of neural networks have been debated for some time.
Clustering of solutions in the symmetric binary perceptron
The geometrical features of the (non-convex) loss landscape of neural network models are crucial in ensuring successful optimization and, most importantly, the capability to generalize well.
Generalized Approximate Survey Propagation for High-Dimensional Estimation
In Generalized Linear Estimation (GLE) problems, we seek to estimate a signal that is observed through a linear transform followed by a component-wise, possibly nonlinear and noisy, channel.
Critical initialisation in continuous approximations of binary neural networks
Moreover, we predict theoretically and confirm numerically, that common weight initialisation schemes used in standard continuous networks, when applied to the mean values of the stochastic binary weights, yield poor training performance.
On the role of synaptic stochasticity in training low-precision neural networks
Stochasticity and limited precision of synaptic weights in neural network models are key aspects of both biological and hardware modeling of learning processes.
Unreasonable Effectiveness of Learning Neural Networks: From Accessible States and Robust Ensembles to Basic Algorithmic Schemes
We define a novel measure, which we call the "robust ensemble" (RE), which suppresses trapping by isolated configurations and amplifies the role of these dense regions.
Learning may need only a few bits of synaptic precision
Learning in neural networks poses peculiar challenges when using discretized rather then continuous synaptic states.
Local entropy as a measure for sampling solutions in Constraint Satisfaction Problems
We introduce a novel Entropy-driven Monte Carlo (EdMC) strategy to efficiently sample solutions of random Constraint Satisfaction Problems (CSPs).
Subdominant Dense Clusters Allow for Simple Learning and High Computational Performance in Neural Networks with Discrete Synapses
We also show that the dense regions are surprisingly accessible by simple learning protocols, and that these synaptic configurations are robust to perturbations and generalize better than typical solutions.
