Abstract: Modern Siamese trackers mainly rely on classifying and regressing pre-defined anchor boxes or per-pixel points, which are assigned as positive and negative samples based on box ...

Probabilistic Programming is a way of defining probabilistic models by overloading the operations in standard programming language to have probabilistic meanings. The goal is to specify probabilistic ...

We study machine learning formulations of inductive program synthesis; given input-output examples, we try to synthesize source code that maps inputs to corresponding outputs. Our aims are to develop ...

Artificial intelligence and machine learning could become dramatically more efficient, thanks to a new type of computer component developed by researchers at the University of California, Santa ...

Abstract: Probabilistic graphical models are a fundamen-tal tool for modeling uncertainty and statistical dependencies in various domains, making them indispensable for decision-making, machine ...