Towards understanding sparse filtering: A theoretical perspective
FM Zennaro, K Chen - Neural Networks, 2018 - Elsevier
In this paper we present a theoretical analysis to understand sparse filtering, a recent and
effective algorithm for unsupervised learning. The aim of this research is not to show …
effective algorithm for unsupervised learning. The aim of this research is not to show …
Learning to adapt by minimizing discrepancy
We explore whether useful temporal neural generative models can be learned from
sequential data without back-propagation through time. We investigate the viability of a more …
sequential data without back-propagation through time. We investigate the viability of a more …
[图书][B] Feature Distribution Learning for Covariate Shift Adaptation Using Sparse Filtering
FM Zennaro - 2017 - search.proquest.com
This thesis studies a family of unsupervised learning algorithms called feature distribution
learning and their extension to perform covariate shift adaptation. Unsupervised learning is …
learning and their extension to perform covariate shift adaptation. Unsupervised learning is …
[图书][B] Coordinated Local Learning Algorithms for Continuously Adaptive Neural Systems
AG Ororbia II - 2018 - search.proquest.com
It is common statistical learning practice to build models, nowadays largely connectionist
models, on very large, static, and fully annotated datasets of identically and independently …
models, on very large, static, and fully annotated datasets of identically and independently …