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03/02/2023
Optimal transfer protocol by incremental layer defrosting
Transfer learning is a powerful tool enabling model training with limite...
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05/31/2022
Inducing bias is simpler than you think
Machine learning may be oblivious to human bias but it is not immune to ...
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06/15/2021
An Analytical Theory of Curriculum Learning in Teacher-Student Networks
In humans and animals, curriculum learning – presenting data in a curate...
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06/09/2021
Probing transfer learning with a model of synthetic correlated datasets
Transfer learning can significantly improve the sample efficiency of neu...
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12/01/2020
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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12/09/2019
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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05/13/2019
Generalized Approximate Survey Propagation for High-Dimensional Estimation
In Generalized Linear Estimation (GLE) problems, we seek to estimate a s...
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10/28/2018
Gaussian Process Prior Variational Autoencoders
Variational autoencoders (VAE) are a powerful and widely-used class of m...
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10/26/2017
On the role of synaptic stochasticity in training low-precision neural networks
Stochasticity and limited precision of synaptic weights in neural networ...
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05/20/2016
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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02/12/2016
Learning may need only a few bits of synaptic precision
Learning in neural networks poses peculiar challenges when using discret...
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11/18/2015
Local entropy as a measure for sampling solutions in Constraint Satisfaction Problems
We introduce a novel Entropy-driven Monte Carlo (EdMC) strategy to effic...
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09/18/2015