The data sets available to train models for in silico drug discovery efforts are often small.
Indeed, the sparse availability of labeled data is a major barrier to artificial-intelligence …

Large-scale labeled training datasets have enabled deep neural networks to excel across a
wide range of benchmark vision tasks. However, in many applications, it is prohibitively …

Federated learning (FL) is a machine learning setting where many clients (eg, mobile
devices or whole organizations) collaboratively train a model under the orchestration of a …

A key learning scenario in large-scale applications is that of federated learning, where a
centralized model is trained based on data originating from a large number of clients. We …

For machine learning systems to be reliable, we must understand their performance in
unseen, out-of-distribution environments. In this paper, we empirically show that out-of …

Deep learning has produced state-of-the-art results for a variety of tasks. While such
approaches for supervised learning have performed well, they assume that training and …

In this paper, we propose Test-Time Training, a general approach for improving the
performance of predictive models when training and test data come from different …

Deep learning models have shown their advantage in many different tasks, including
neuroimage analysis. However, to effectively train a high-quality deep learning model, the …