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Graph Random Neural Networks for Semi-Supervised Learning on Graphs
WenzhengFeng, JieZhang, YuxiaoDong....
Published date-12/01/2020
DataAugmentation, NodeClassification
We study the problem of semi-supervised learning on graphs, for which graph neural networks (GNNs) have been extensively explored. However, most existing GNNs inherently suffer from the limitations of over-smoothing, …
Analysis of Drifting Features
FabianHinder, JonathanJakob, BarbaraHammer....
Published date-12/01/2020
FeatureSelection
The notion of concept drift refers to the phenomenon that the distribution, which is underlying the observed data, changes over time. We are interested in an identification of those features, …
Attention-Gated Brain Propagation: How the brain can implement reward-based error backpropagation
IsabellaPozzi, SanderBohte, PieterRoelfsema....
Published date-12/01/2020
ImageClassification
Much recent work has focused on biologically plausible variants of supervised learning algorithms. However, there is no teacher in the motor cortex that instructs the motor neurons and learning in …
The Dilemma of TriHard Loss and an Element-Weighted TriHard Loss for Person Re-Identification
YihaoLv, YouzhiGu, LiuXinggao....
Published date-12/01/2020
PersonRe-Identification
Triplet loss with batch hard mining (TriHard loss) is an important variation of triplet loss inspired by the idea that hard triplets improve the performance of metric leaning networks. However, …
Deep Graph Pose: a semi-supervised deep graphical model for improved animal pose tracking
AnqiWu, E.KellyBuchanan, MatthewWhiteway....
Published date-12/01/2020
PoseTracking, TransferLearning
Noninvasive behavioral tracking of animals is crucial for many scientific investigations. Recent transfer learning approaches for behavioral tracking have considerably advanced the state of the art. Typically these methods treat …
Contextual Reserve Price Optimization in Auctions via Mixed Integer Programming
JoeyHuchette, HaihaoLu, HosseinEsfandiari....
Published date-12/01/2020
We study the problem of learning a linear model to set the reserve price in an auction, given contextual information, in order to maximize expected revenue from the seller side. …