A Theoretical Analysis of Metric Hypothesis Transfer Learning Supplementary Material

A Theoretical Analysis of Metric Hypothesis Transfer Learning

We consider the problem of transferring some a priori knowledge in the context of supervised metric learning approaches. While this setting has been successfully applied in some empirical contexts, no theoretical evidence exists to justify this approach. In this paper, we provide a theoretical justification based on the notion of algorithmic stability adapted to the regularized metric learning ...

LINEAR HYPOTHESIS TESTING USING DLR METRIC

Several practical problems of hypotheses testing can be under a general linear model analysis of variance which would be examined. In analysis of variance, when the response random variable Y , has linear relationship with several random variables X, another important model as analysis of covariance can be used. In this paper, assuming that Y is fuzzy and using DLR metric, a method for testing ...

Task Effectiveness Predictors: Technique Feature Analysis VS. Involvement Load Hypothesis

How deeply a word is processed has long been considered as a crucial factor in the realm of vocabulary acquisition. In literature, two frameworks have been proposed to operationalize the depth of processing, namely the Involvement Load Hypothesis (ILH) and the Technique Feature Analysis (TFA). However, they differ in the way they have operationalized it specially in terms of their attentional c...

Blur-Invariant Deep Learning for Blind-Deblurring (Supplementary Material)

We provide additional quantitative and qualitative comparisons on two more publicly available datasets, Köhler et al. [2] and Lai et al. [3] respectively, in this supplementary material to demonstrate the efficacy of our proposed network. We also provide an analysis on the deblurring efficiency and generalizability of our network when compared to a network learned for a single blur kernel. Alon...

Supplementary Material for : Unsupervised Riemannian Metric Learning for Histograms Using Aitchison Transformations ∗