Technology
Learning to Navigate in Cities Without a Map
Piotr Mirowski, Matt Grimes, Mateusz Malinowski, Karl Moritz Hermann, Keith Anderson, Denis Teplyashin, Karen Simonyan, koray kavukcuoglu, Andrew Zisserman, Raia Hadsell
Neural Information Processing Systems http://nips.cc/
PIDForest: Anomaly Detection via Partial Identification
Parikshit Gopalan, Vatsal Sharan, Udi Wieder
Neural Information Processing Systems http://nips.cc/
Transfusion: UnderstandingTransferLearningfor MedicalImaging
This basic formula has seen almost universal adoption across many different medical specialties.
Point process latent variable models of larval zebrafish behavior
Anuj Sharma, Robert Johnson, Florian Engert, Scott Linderman
Each trial consists ofasequence of upto350swimbouts(b.)
Practical Methods for Graph Two-Sample Testing
Debarghya Ghoshdastidar, Ulrike von Luxburg
We demonstrate the practical merits and limitations of existing theoretical tests and their bootstrapped variants.
eae27d77ca20db309e056e3d2dcd7d69-Paper.pdf
Furthermore, we show that the WAE objective is related to other statistical quantities such as thef-divergence and in particular, upper bounded by the Wasserstein distance, which then allows us to tap into existing efficient(regularized)optimaltransportsolvers.
Mixed Samples as Probes for Unsupervised Model Selection in Domain Adaptation
In this paper, we present MixV al, an innovative model selection method that operates solely with unlabeled target data during inference.
Universal Invariant and Equivariant Graph Neural Networks
Nicolas Keriven, Gabriel Peyré
More precisely, we consider networks with a single hidden layer,obtained bysumming channels formed byapplying anequivariant linear operator, a pointwise non-linearity, and either an invariant or equivariant linearoutputlayer.