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 ...

Probabilistic programming is an approach to computing based on the idea that probabilistic models can be naturally and efficiently represented as executable code. This idea has enabled researchers to ...

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. Satellite data provides essential insights into the spatiotemporal distribution of CO ...

Functional programming, as the name implies, is about functions. While functions are part of just about every programming paradigm, including JavaScript, a functional programmer has unique ...

This tutorial will introduce a new paradigm for agent-based models (ABMs) that leverages automatic differentiation (AD) to efficiently compute simulator gradients. In particular, this tutorial will ...

We consider the computable content of several key theorems in probability theory, and discuss their implications for the design of probabilistic programming languages. A random variable is said to be ...

COPENHAGEN, Denmark, June 25, 2021 (GLOBE NEWSWIRE) -- Evaxion Biotech A/S (Nasdaq: EVAX), a clinical-stage biotechnology company specializing in the development of AI-driven immunotherapies to ...

Abstract: The advent of the Internet in the late 1990s led to the increased flood of data termed Big Data. To derive meaningful value from big data, specialized tools and techniques are required.

In this article we give an introduction to the Probabilistic Programming (PP) paradigm for .NET engineers. We start by explaining the differences between PP and traditional approaches and show a ...