In this community review report, we discuss applications and techniques for fast machine
learning (ML) in science—the concept of integrating powerful ML methods into the real-time …

Inspired by the long-range modeling ability of ViTs large-kernel convolutions are widely
studied and adopted recently to enlarge the receptive field and improve model performance …

MetaFormer, the abstracted architecture of Transformer, has been found to play a significant
role in achieving competitive performance. In this paper, we further explore the capacity of …

Transformers have shown great potential in computer vision tasks. A common belief is their
attention-based token mixer module contributes most to their competence. However, recent …

Recent studies show that transformer has strong capability of building long-range
dependencies, yet is incompetent in capturing high frequencies that predominantly convey …

This chapter provides approaches to the problem of quantizing the numerical values in deep
Neural Network computations, covering the advantages/disadvantages of current methods …

Mamba, an architecture with RNN-like token mixer of state space model (SSM), was recently
introduced to address the quadratic complexity of the attention mechanism and …

Part defects and irregularities that influence the part quality is an especially large problem in
additive manufacturing (AM) processes such as selective laser sintering (SLS). Destructive …

Deep learning is gaining attention in data-driven remaining useful life (RUL) prediction of
machinery because of its powerful representation learning ability. With the help of deep …

We present an interpretation of Inception modules in convolutional neural networks as being
an intermediate step in-between regular convolution and the depthwise separable …