Training machine learning models in a meaningful order, from the easy samples to the hard
ones, using curriculum learning can provide performance improvements over the standard …

Emotion recognition in conversation (ERC) aims to detect the emotion label for each
utterance. Motivated by recent studies which have proven that feeding training examples in …

The superior performance of modern deep networks usually comes with a costly training
procedure. This paper presents a new curriculum learning approach for the efficient training …

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 (CL) is a machine learning paradigm gradually learning from easy to
hard, which is inspired by human curricula. As an easy-to-use and general training strategy …

We propose Curriculum by Masking (CBM), a novel state-of-the-art curriculum learning
strategy that effectively creates an easy-to-hard training schedule via patch (token) masking …

We propose iterative algorithms to solve adversarial problems in a variety of supervised
learning settings of interest. Our algorithms, which can be interpreted as suitable ascent …

Loss functions are essential to computing the divergence of a model's predicted distribution
from the ground truth. Such functions play a vital role in machine learning algorithms as they …

The superior performance of modern computer vision backbones (eg, vision Transformers
learned on ImageNet-1K/22K) usually comes with a costly training procedure. This study …

Abstract Domain Generalization (DG) aims to learn a model from a labeled set of source
domains which can generalize to an unseen target domain. Although an important stepping …