Modern deep neural networks, particularly recent large language models, come with
massive model sizes that require significant computational and storage resources. To …

Image classification has always been a hot research direction in the world, and the
emergence of deep learning has promoted the development of this field. Convolutional …

Human skeleton-based action recognition offers a valuable means to understand the
intricacies of human behavior because it can handle the complex relationships between …

Large language models based on transformers have achieved great empirical successes.
However, as they are deployed more widely, there is a growing need to better understand …

Large-scale pre-trained models (PTMs) such as BERT and GPT have recently achieved
great success and become a milestone in the field of artificial intelligence (AI). Owing to …

Currently, cross-scene hyperspectral image (HSI) classification has drawn increasing
attention. It is necessary to train a model only on source domain (SD) and directly …

Artificial intelligence research has seen enormous progress over the past few decades, but it
predominantly relies on fixed datasets and stationary environments. Continual learning is an …

This paper introduces WaveGrad, a conditional model for waveform generation which
estimates gradients of the data density. The model is built on prior work on score matching …

Machine learning is frequently listed among the most promising applications for quantum
computing. This is in fact a curious choice: the machine-learning algorithms of today are …

This textbook establishes a theoretical framework for understanding deep learning models
of practical relevance. With an approach that borrows from theoretical physics, Roberts and …