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Found 6 papers, 2 papers with code
A Mathematical Model for Curriculum Learning for Parities
Curriculum learning (CL) - training using samples that are generated and presented in a meaningful order - was introduced in the machine learning context around a decade ago.
Learning to Reason with Neural Networks: Generalization, Unseen Data and Boolean Measures
More generally, the paper considers the learning of logical functions with gradient descent (GD) on neural networks.
Learning curves for the multi-class teacher-student perceptron
For Gaussian teacher weights, we investigate the performance of ERM with both cross-entropy and square losses, and explore the role of ridge regularisation in approaching Bayes-optimality.
An initial alignment between neural network and target is needed for gradient descent to learn
This paper introduces the notion of ``Initial Alignment'' (INAL) between a neural network at initialization and a target function.
Regularization by Misclassification in ReLU Neural Networks
We study the implicit bias of ReLU neural networks trained by a variant of SGD where at each step, the label is changed with probability $p$ to a random label (label smoothing being a close variant of this procedure).
Stochastic block model entropy and broadcasting on trees with survey
The limit of the entropy in the stochastic block model (SBM) has been characterized in the sparse regime for the special case of disassortative communities [COKPZ17] and for the classical case of assortative communities but in the dense regime [DAM16].
Probability Information Theory Information Theory
