Deep supervised learning algorithms typically require a large volume of labeled data to
achieve satisfactory performance. However, the process of collecting and labeling such data …

Computational pathology is revolutionizing the field of pathology by integrating advanced
computer vision and machine learning technologies into diagnostic workflows. It offers …

In recent decades, wildfires have caused tremendous property losses, fatalities, and
extensive damage to forest ecosystems. Inspired by the abundance of publicly available …

The rapid release of high-performing computer vision models offers new potential to study
the impact of different inductive biases on the emergent brain alignment of learned …

Self-supervised learning (SSL) has emerged as a powerful framework to learn
representations from raw data without supervision. Yet in practice, engineers face issues …

We present a simple picture of the training process of self-supervised learning methods with
dual deep networks. In our picture, these methods learn their high-dimensional embeddings …

Self-supervised learning (SSL) has recently allowed leveraging large datasets of unlabeled
speech signals to reach impressive performance on speech tasks using only small amounts …

Spatial transcriptomics enables interrogating the molecular composition of tissue with ever-
increasing resolution and sensitivity. However, costs, rapidly evolving technology, and lack …

Developing self-supervised learning (SSL) models that can learn universal and transferable
representations of H&E gigapixel whole-slide images (WSIs) is becoming increasingly …

The rapid development and open-source release of highly performant computer vision
models offers new potential for examining how different inductive biases impact …