Detecting out-of-distribution examples is important for safety-critical machine learning
applications such as detecting novel biological phenomena and self-driving cars. However …

The goal of this paper is open-vocabulary object detection (OVOD)—building a model that
can detect objects beyond the set of categories seen at training, thus enabling the user to …

Recent research on robustness has revealed significant performance gaps between neural
image classifiers trained on datasets that are similar to the test set, and those that are from a …

Machine learning models often perform poorly on subgroups that are underrepresented in
the training data. Yet, little is understood on the variation in mechanisms that cause …

Geometric descriptions of deep neural networks (DNNs) have the potential to uncover core
representational principles of computational models in neuroscience. Here we examined the …

Self-supervised learning on large-scale Vision Transformers (ViTs) as pre-training methods
has achieved promising downstream performance. Yet, how much these pre-training …

The top-k classification accuracy is one of the core metrics in machine learning. Here, k is
conventionally a positive integer, such as 1 or 5, leading to top-1 or top-5 training objectives …

Automated medical image analysis systems often require large amounts of training data with
high quality labels, which are difficult and time consuming to generate. This paper …

Plankton microorganisms play a huge role in the aquatic food web. Recently, it has been
proposed to use plankton as a biosensor, since they can react to even minimal perturbations …

The coronary angiogram is the gold standard for evaluating the severity of coronary artery
disease stenoses. Presently, the assessment is conducted visually by cardiologists, a …