Top Tweets for #WassersteinDistance
just in time for @ECMLPKDD. With the growing focus on CV fairness, including #MetaAI recent #DINOv2 and #FACET, we're eager to contribute to this important topic #CVFairness #ECMPLPKDD #wassersteindistance @FabbrizziSimone @sympap @NoBIAS_ITN @mammoth_ai
"Studying bias in visual features through the lens of optimal transport" by Simone Fabbrizzi, Xuan Zhao, Emmanouil Krasanakis, Symeon Papadopoulos & @entoutsi (https://t.co/K1hpzRlhfF) (OA)
Dr. Sarmad Majeed #NUST proposed an efficient #scenarioreduction network based on #Wassersteindistance & #neuralnetworks which can handle large-scale scenarios for enhanced #renewable #powersystems and improved accuracy. @IEEEXplore #NUSTResearch
https://t.co/ordkIUIDrx

Frank Nielsen, Gautier Marti, Sumanta Ray, Saumyadipta Pyne
"Clustering Patterns Connecting COVID-19 Dynamics and Human Mobility Using Optimal Transport"
#WassersteinDistance #OptimalTransport
Sankhya B, 83, 167β184 (2021) @SpringerStats
π#freeaccess https://t.co/3od4LHbSPi
π‘Amari_Karakida_Oizumi (2018) https://t.co/37psSIZAu8 has been downloaded over 11,000 times so far. Recently it was cited in the following earth science paper by I E Mulia et al. @Caltech
#Wassersteindistance
Earth and Space Science (2022)
π#openaccess
https://t.co/TZRIVXToxd
Gangbo, W., Jekel, D., Nam, K., Shlyakhtenko, D.:
"Duality for Optimal Couplings in Free Probability"
@UCLA @UCSanDiego @kaistmath
#quantuminformationtheory #Wassersteindistance
#Voiculescu
Commun. Math. Phys. (12 Sep 2022)
π#openaccess
https://t.co/yrgJ7Mu8bD
Reached a milestone of 10K download!π
Shun-ichi Amari, Ryo Karakida, Masafumi Oizumi
@riken_en @AIST_EN
"#InformationGeometry connecting #Wassersteindistance and KullbackβLeibler divergence via the entropy-relaxed transportation problem"
π#openaccess https://t.co/37psSIHrg0
If you want to understand #WassersteinDistance function, check this article by @cathalhoran.
He uses plenty of examples to explain the concept - from very simple ones like crossing the park, to more NLP-related such as word embeddings.
https://t.co/kMULvKpa6d
Want to understand if #WassersteinDistance can be applied to your specific #ML task?
Read this post, where @cathalhoran explains this distance function, shows examples of its application, and compares it to another metric.
https://t.co/kMULvK7yHD
Interested in #NLP?
Check this article by @cathalhoran and explore how #WassersteinDistance can be used to measure the similarity between two different text sequences.
https://t.co/kMULvKpa6d
Interactive Sketch-Based Normal Map Generation with Deep
#NeuralNetworks
https://t.co/bAiC8LPvID
#Sketch #NormalMap #PointHints #GenerativeAdversarialNetwork #WassersteinDistance
More-flexible #machinelearning http://t.co/dpi3eIuRDC #ml #analytics #semanticsimilarity #MIT #WassersteinDistance #supervised #Flickr
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