量子に触発されたフォレスト : 機械学習に関する物理的な視点
Zeke Xie
Machine learning has gained in popularity over recent years.It shows great power in many real-world problems, including Computer Vision, Natural Language Processing and Speech Recognition.However, a clear theoretical mechanism is still lacked behind many machine learning algorithms, such as Deep Learning.Good theoretical analysis can not only explain the current success of machine learning, but also reveal new approaches to future advancements.In last few years, more and more physicists contributed good works that provided novel insights and philosophy for machine learning communities.Due to two reasons, we believe physical approaches can be very promising.First, we think machine learning share some similar theoretical mechanism with physical theories, particularly quantum physics and statistical physics.We may employ physical languages to study machine learning models in some physical frameworks.Second, real-world problems and information itself must obey some fundamental laws, which are often physical laws for our universe.Good physical prior knowledge which may be helpful to reduce model complexity and improve generalization.The physical perspective deserve more attention.In this thesis, we review a few related works on the physics based approach and related traditional machine learning.It helps us understand how physics may interact with machine learning.We propose quantum interpretations for machine learning and a class of original physics-inspired machine learning algorithms, named Quantum-Inspired Forest.Both theoretical proof and empirical analysis are presented in details.The success of quantum-inspired machine learning encourages us to pay attention to multiple physical viewpoints.Even without solid theoretical analysis, important prior knowledge and heuristics may be obtained from physical models.The proposed algorithm is an example that illustrates how to combine physics and machine learning together.We in particular discuss a physical perspective on machine learning.We hope our physical analysis on machine learning would inspire more novel works in near future.
•Sep 15, 2017•0 citations