976 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Centre National de la Recherche ScientifiqueFR49 papers
- Technical University of DenmarkDK38 papers
- Aarhus UniversityDK29 papers
- University of CopenhagenDK23 papers
- Tokyo Institute of TechnologyJP19 papers
- University of ManchesterGB18 papers
- École Polytechnique Fédérale de LausanneCH17 papers
- University of OxfordGB16 papers
- Centre de Physique ThéoriqueFR14 papers
- East China Normal UniversityCN14 papers
- IMAR - Institutul de Matematică „Simion Stoilow” al Academiei RomâneRO14 papers
- Imperial College LondonGB14 papers
5 papers · 2 filters
Advanced Dropout: A Model-free Methodology for Bayesian Dropout Optimization
Jiyang Xie, Zhanyu Ma, and Jianjun Lei +4
Due to lack of data, overfitting ubiquitously exists in real-world applications of deep neural networks (DNNs). We propose advanced dropout, a model-free methodology, to mitigate o…
Force myography benchmark data for hand gesture recognition and transfer learning
Thomas Buhl Andersen, Rógvi Eliasen, Mikkel Jarlund +1
Force myography has recently gained increasing attention for hand gesture recognition tasks. However, there is a lack of publicly available benchmark data, with most existing studi…
A general framework for defining and optimizing robustness
Alessandro Tibo, Manfred Jaeger, Kim G. Larsen
Robustness of neural networks has recently attracted a great amount of interest. The many investigations in this area lack a precise common foundation of robustness concepts. There…
Relational Fusion Networks: Graph Convolutional Networks for Road Networks
Tobias Skovgaard Jepsen, Christian S. Jensen, Thomas Dyhre Nielsen
The application of machine learning techniques in the setting of road networks holds the potential to facilitate many important intelligent transportation applications. Graph Convo…
CAUSE: Learning Granger Causality from Event Sequences using Attribution Methods
Wei Zhang, Thomas Kobber Panum, Somesh Jha +2
We study the problem of learning Granger causality between event types from asynchronous, interdependent, multi-type event sequences. Existing work suffers from either limited mode…