The field of deep learning has witnessed significant progress, particularly in computer vision
(CV), natural language processing (NLP), and speech. The use of large-scale models …

We propose and analyze algorithms for distributionally robust optimization of convex losses
with conditional value at risk (CVaR) and $\chi^ 2$ divergence uncertainty sets. We prove …

Training on web-scale data can take months. But much computation and time is wasted on
redundant and noisy points that are already learnt or not learnable. To accelerate training …

The computation necessary for training Transformer-based language models has
skyrocketed in recent years. This trend has motivated research on efficient training …

Effective semi-supervised learning (SSL) in medical image analysis (MIA) must address two
challenges: 1) work effectively on both multi-class (eg, lesion classification) and multi-label …

Inspired by human learning, researchers have proposed ordering examples during training
based on their difficulty. Both curriculum learning, exposing a network to easier examples …

The striking fractal geometry of strange attractors underscores the generative nature of
chaos: like probability distributions, chaotic systems can be repeatedly measured to produce …

The field of deep learning has witnessed significant progress in recent times, particularly in
areas such as computer vision (CV), natural language processing (NLP), and speech. The …

Recent works have revealed an essential paradigm in designing loss functions that
differentiate individual losses versus aggregate losses. The individual loss measures the …

Selective classification allows models to abstain from making predictions (eg, say" I don't
know") when in doubt in order to obtain better effective accuracy. While typical selective …