Extracting Interpretable Physical Parameters from Spatiotemporal Systems Using Unsupervised Learning
نویسندگان
چکیده
منابع مشابه
Unsupervised Learning of Disentangled and Interpretable Representations from Sequential Data
We present a factorized hierarchical variational autoencoder, which learns disentangled and interpretable representations from sequential data without supervision. Specifically, we exploit the multi-scale nature of information in sequential data by formulating it explicitly within a factorized hierarchical graphical model that imposes sequence-dependent priors and sequence-independent priors to...
متن کاملExtracting Interpretable Models from Matrix Factorization Models
Matrix factorization models have been successfully used in many real-world tasks, such as knowledge base completion and recommendation systems. However, explaining the causes that elicit a particular prediction by a manual inspection of its latent representations is a difficult task. In this paper we try to overcome this problem by exploring descriptive model classes in their ability to faithfu...
متن کاملInterpretable clustering using unsupervised binary trees
We herein introduce a new method of interpretable clustering that uses unsupervised binary trees. It is a three-stage procedure, the first stage of which entails a series of recursive binary splits to reduce the heterogeneity of the data within the new subsamples. During the second stage (pruning), consideration is given to whether adjacent nodes can be aggregated. Finally, during the third sta...
متن کاملExtracting Chargino/neutralino Mass Parameters from Physical Observables
I report on two papers, hep-ph/9806279 and hep-ph/9807336, where complementary strategies are proposed for the determination of the chargino/neutralino sector parameters, M1,M2, μ and tanβ, from the knowledge of some physical observables. This determination and the occurrence of possible ambiguities are studied as far as possible analytically within the context of the unconstrained MSSM, assumi...
متن کاملUnsupervised Topographic Learning for Spatiotemporal Data Mining
In recent years, the size and complexity of datasets have shown an exponential growth. In many application areas, huge amounts of data are generated, explicitly or implicitly containing spatial or spatiotemporal information. However, the ability to analyze these data remains inadequate, and the need for adapted data mining tools becomes a major challenge. In this paper, we propose a new unsuper...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Physical Review X
سال: 2020
ISSN: 2160-3308
DOI: 10.1103/physrevx.10.031056