Convolutional neural networks (CNNs) were inspired by early findings in the study of
biological vision. They have since become successful tools in computer vision and state-of …

Advances in experimental neuroscience have transformed our ability to explore the structure
and function of neural circuits. At the same time, advances in machine learning have …

Deep learning algorithms trained to predict masked words from large amount of text have
recently been shown to generate activations similar to those of the human brain. However …

The ongoing exponential rise in recording capacity calls for new approaches for analysing
and interpreting neural data. Effective dimensionality has emerged as an important property …

What mechanisms underlie flexible inter-areal communication in the cortex? We consider
four mechanisms for temporal coordination and their contributions to communication:(1) …

Deep neural networks (DNNs) are machine learning algorithms that have revolutionized
computer vision due to their remarkable successes in tasks like object classification and …

In label-noise learning, estimating the transition matrix has attracted more and more
attention as the matrix plays an important role in building statistically consistent classifiers …

To address the challenges of long-tailed classification, researchers have proposed several
approaches to reduce model bias, most of which assume that classes with few samples are …

When training overparameterized deep networks for classification tasks, it has been widely
observed that the learned features exhibit a so-called" neural collapse'" phenomenon. More …

Understanding the reasons for the success of deep neural networks trained using stochastic
gradient-based methods is a key open problem for the nascent theory of deep learning. The …