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ASurprisinglySimpleApproachto GeneralizedFew-ShotSemanticSegmentation

Neural Information Processing Systems

Inthis paper,wepropose asimple yet effectivemethod for GFSS that does not use the techniques mentioned above. Also, wetheoretically show that our method perfectly maintains the segmentation performance of the base-class modelovermostofthebaseclasses. Through numerical experiments, we demonstrated the effectiveness of our method. It improved in novel-class segmentation performance in the1-shot scenario by6.1% on the PASCAL-5i dataset,4.7%on


ProbabilisticMissingValueImputation forMixedCategoricalandOrderedData

Neural Information Processing Systems

Social survey datasets, for example, are typically mixed because they include variables like age (continuous), demographic group (categorical), and Likert scales (ordinal) measuring how strongly a respondent agrees with certain stated opinions. Continuous variables are encoded as real numbers and sometimes called numeric. We refer to variables that admit a total order (e.g.


Will the Gulf's push for its own AI succeed?

The Guardian

Will the Gulf's push for its own AI succeed? That, and US tech giants' plans to spend more than $600bn this year alone. Can the Gulf states capture some of the US's tech dominance for themselves? I spent most of last week in Doha at the Web Summit Qatar, the Gulf's new version of the popular annual tech conference. One theme stood out among the speeches I watched and the conversations I had: sovereignty.



VariationalInferenceforContinuous-Time SwitchingDynamicalSystems

Neural Information Processing Systems

Since many areas, such as biology or discrete-event systems, are naturally described in continuous time, we present a model based on a Markov jumpprocessmodulating asubordinated diffusionprocess. Weprovidetheexact evolution equations fortheprior andposterior marginal densities, thedirect solutions of which are however computationally intractable.