Results 201 to 210 of about 2,509,053 (235)

Wasserstein Distance Rivals Kullback-Leibler Divergence for Knowledge Distillation

Neural Information Processing Systems
Since pioneering work of Hinton et al., knowledge distillation based on Kullback-Leibler Divergence (KL-Div) has been predominant, and recently its variants have achieved compelling performance.
Jiaming Lv, Haoyuan Yang, Peihua Li
semanticscholar   +1 more source

The Z-Gromov-Wasserstein Distance

Journal of machine learning research
The Gromov-Wasserstein (GW) distance is a powerful tool for comparing metric measure spaces which has found broad applications in data science and machine learning. Driven by the need to analyze datasets whose objects have increasingly complex structure (
Martin Bauer, Facundo M'emoli, Tom Needham, Mao Nishino   +3 more
semanticscholar   +1 more source

On distributionally robust chance constrained programs with Wasserstein distance

Mathematical programming, 2018
This paper studies a distributionally robust chance constrained program (DRCCP) with Wasserstein ambiguity set, where the uncertain constraints should be satisfied with a probability at least a given threshold for all the probability distributions of the
Weijun Xie
semanticscholar   +1 more source

The Wasserstein distances

2009
Assume, as before, that you are in charge of the transport of goods between producers and consumers, whose respective spatial distributions are modeled by probability measures.
openaire   +1 more source

Clustering Linear Models Using Wasserstein Distance

2009
This paper deals with the clustering of complex data. The input elements to be clustered are linear models estimated on samples arising from several sub-populations (typologies of individuals). We review the main approaches to the computation of metrics between linear models. We propose to use a Wasserstein based metric for the first time in this field.
IRPINO, Antonio, VERDE, Rosanna
openaire   +3 more sources

An extended Exp-TODIM method for multiple attribute decision making based on the Z-Wasserstein distance

Expert systems with applications, 2022
Hong Sun, Zhen Yang, Qiang Cai, G. Wei, Zhiwen Mo   +4 more
semanticscholar   +1 more source

Wasserstein distances and curves in the Wasserstein spaces

2015
In this chapter we use the minimal value of transport problems between two probabilities in order to define a distance on the space of probabilities. We mainly consider costs of the form \(c(x,y) = \vert x - y\vert ^{p}\) in \(\varOmega \subset \mathbb{R}^{d}\). We analyze the properties of the distance (called Wasserstein distance) that it defines, in
openaire   +1 more source

Data augmentation strategy for power inverter fault diagnosis based on wasserstein distance and auxiliary classification generative adversarial network

Reliability Engineering & System Safety, 2023
Quan Sun, Fei Peng, Xianghai Yu, Hongsheng Li   +3 more
semanticscholar   +1 more source

Sensitivity of Multiperiod Optimization Problems with Respect to the Adapted Wasserstein Distance

SIAM Journal on Financial Mathematics, 2023
Daniel Bartl, J. Wiesel
semanticscholar   +1 more source

Unsupervised Learning Model of Sparse Filtering Enhanced Using Wasserstein Distance for Intelligent Fault Diagnosis

Journal of Vibration Engineering & Technologies, 2022
Govind Vashishtha, Rajesh Kumar
semanticscholar   +1 more source