
An Analytical Theory of Curriculum Learning in TeacherStudent Networks
In humans and animals, curriculum learning – presenting data in a curate...
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Probing transfer learning with a model of synthetic correlated datasets
Transfer learning can significantly improve the sample efficiency of neu...
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Solvable Model for Inheriting the Regularization through Knowledge Distillation
In recent years the empirical success of transfer learning with neural n...
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Large deviations for the perceptron model and consequences for active learning
Active learning is a branch of machine learning that deals with problems...
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Generalized Approximate Survey Propagation for HighDimensional Estimation
In Generalized Linear Estimation (GLE) problems, we seek to estimate a s...
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Gaussian Process Prior Variational Autoencoders
Variational autoencoders (VAE) are a powerful and widelyused class of m...
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On the role of synaptic stochasticity in training lowprecision neural networks
Stochasticity and limited precision of synaptic weights in neural networ...
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Unreasonable Effectiveness of Learning Neural Networks: From Accessible States and Robust Ensembles to Basic Algorithmic Schemes
In artificial neural networks, learning from data is a computationally d...
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Learning may need only a few bits of synaptic precision
Learning in neural networks poses peculiar challenges when using discret...
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Local entropy as a measure for sampling solutions in Constraint Satisfaction Problems
We introduce a novel Entropydriven Monte Carlo (EdMC) strategy to effic...
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Subdominant Dense Clusters Allow for Simple Learning and High Computational Performance in Neural Networks with Discrete Synapses
We show that discrete synaptic weights can be efficiently used for learn...
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Luca Saglietti
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