We study a novel curriculum learning scheme where in each round, samples are selected to
achieve the greatest progress and fastest learning speed towards the ground-truth on all …

A good teacher can adjust the curriculum based on students' learning history. By analogy, in
this paper, we study the dynamics of a deep neural network's (DNN) performance on …

We provide theoretical investigation of curriculum learning in the context of stochastic
gradient descent when optimizing the convex linear regression loss. We prove that the rate …

Curriculum Learning is motivated by human cognition, where teaching often involves
gradually exposing the learner to examples in a meaningful order, from easy to hard …

Most curriculum learning methods require an approach to sort the data samples by difficulty,
which is often cumbersome to perform. In this work, we propose a novel curriculum learning …

Curriculum learning is a machine learning technique inspired by the way humans acquire
knowledge and skills: by mastering simple concepts first, and progressing through …

Curriculum learning is a technique to improve a model performance and generalization
based on the idea that easy samples should be presented before difficult ones during …

The learning rate (LR) schedule is one of the most important hyper-parameters needing
careful tuning in training DNNs. However, it is also one of the least automated parts of …

Curriculum learning (CL) is a training strategy that trains a machine learning model from
easier data to harder data, which imitates the meaningful learning order in human curricula …

The learning rate is one of the most important hyper-parameters for model training and
generalization. However, current hand-designed parametric learning rate schedules offer …