AI Technology & Industry Review
A research team from the University of California, Princeton University and ETH Zurich proposes RLQP, an accelerated QP solver based on operator-splitting QP (OSQP) that uses deep reinforcement learning (RL) to speed up the solver’s convergence rate.
A research team from Google Research combines the benefits of implicit differentiation and autodiff and proposes a unified, efficient and modular approach for implicit differentiation of optimization problems.
Probability theory is a mathematical framework for quantifying our uncertainty about the world, and is a fundamental building block in the study of machine learning. The purpose of this article is to provide the vocabulary and mathematics needed before applying probability theory to machine learning tasks.
This is the second in a special Synced series of introductory articles on traditionally theoretical fields of studies and their impact on modern-day machine learning.
To apply AI technology to the oil and gas industry, oil companies and startups generally first establish either a research group or a research center for the purpose. AI for oil and gas is a huge potential market, expected to reach US$2.85 billion by 2022.
RECOS model supply us with a signal analysis perspective of Convolution Neural Network. From this perspective, we can see the effectiveness of activation and deep architectures.