SY Chung, LF Abbott - Current opinion in neurobiology, 2021 - Elsevier
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) …
FA Wichmann, R Geirhos - Annual Review of Vision Science, 2023 - annualreviews.org
Deep neural networks (DNNs) are machine learning algorithms that have revolutionized computer vision due to their remarkable successes in tasks like object classification and …
D Cheng, T Liu, Y Ning, N Wang… - Proceedings of the …, 2022 - openaccess.thecvf.com
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 …
C Yaras, P Wang, Z Zhu… - Advances in neural …, 2022 - proceedings.neurips.cc
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 …