Deep learning has become a popular tool for medical image analysis, but the limited
availability of training data remains a major challenge, particularly in the medical field where …

The field of machine learning is witnessing its golden era as deep learning slowly becomes
the leader in this domain. Deep learning uses multiple layers to represent the abstractions of …

Meta-learning for few-shot learning entails acquiring a prior over previous tasks and
experiences, such that new tasks be learned from small amounts of data. However, a critical …

Neuroscience research is undergoing a minor revolution. Recent advances in machine
learning and artificial intelligence research have opened up new ways of thinking about …

Generating video frames that accurately predict future world states is challenging. Existing
approaches either fail to capture the full distribution of outcomes, or yield blurry generations …

Deep learning has exploded in the public consciousness, primarily as predictive and
analytical products suffuse our world, in the form of numerous human-centered smart-world …

There are many forms of feature information present in video data. Principle among them are
object identity information which is largely static across multiple video frames, and object …

Abstract We introduce the Neural State Machine, seeking to bridge the gap between the
neural and symbolic views of AI and integrate their complementary strengths for the task of …

Neuroscience has focused on the detailed implementation of computation, studying neural
codes, dynamics and circuits. In machine learning, however, artificial neural networks tend …

A key advance in learning generative models is the use of amortized inference distributions
that are jointly trained with the models. We find that existing training objectives for variational …