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
13 papers · 2 filters
Unsupervised Time Series Outlier Detection with Diversity-Driven Convolutional Ensembles -- Extended Version
David Campos, Tung Kieu, Chenjuan Guo +4
With the sweeping digitalization of societal, medical, industrial, and scientific processes, sensing technologies are being deployed that produce increasing volumes of time series…
Active Learning of Markov Decision Processes using Baum-Welch algorithm (Extended)
Giovanni Bacci, Anna Ingólfsdóttir, Kim Larsen +1
Cyber-physical systems (CPSs) are naturally modelled as reactive systems with nondeterministic and probabilistic dynamics. Model-based verification techniques have proved effective…
On Addressing Heterogeneity in Federated Learning for Autonomous Vehicles Connected to a Drone Orchestrator
Igor Donevski, Jimmy Jessen Nielsen, Petar Popovski
In this paper we envision a federated learning (FL) scenario in service of amending the performance of autonomous road vehicles, through a drone traffic monitor (DTM), that also ac…
Unsupervised Path Representation Learning with Curriculum Negative Sampling
Sean Bin Yang, Chenjuan Guo, Jilin Hu +2
Path representations are critical in a variety of transportation applications, such as estimating path ranking in path recommendation systems and estimating path travel time in nav…
HIFI: Anomaly Detection for Multivariate Time Series with High-order Feature Interactions
Liwei Deng, Xuanhao Chen, Yan Zhao +1
Monitoring complex systems results in massive multivariate time series data, and anomaly detection of these data is very important to maintain the normal operation of the systems.…
Closed-form Continuous-time Neural Models
Ramin Hasani, Mathias Lechner, Alexander Amini +5
Continuous-time neural processes are performant sequential decision-makers that are built by differential equations (DE). However, their expressive power when they are deployed on…
Chebyshev-Cantelli PAC-Bayes-Bennett Inequality for the Weighted Majority Vote
Yi-Shan Wu, Andrés R. Masegosa, Stephan S. Lorenzen +2
We present a new second-order oracle bound for the expected risk of a weighted majority vote. The bound is based on a novel parametric form of the Chebyshev- Cantelli inequality (a…
SpecRepair: Counter-Example Guided Safety Repair of Deep Neural Networks
Fabian Bauer-Marquart, David Boetius, Stefan Leue +1
Deep neural networks (DNNs) are increasingly applied in safety-critical domains, such as self-driving cars, unmanned aircraft, and medical diagnosis. It is of fundamental importanc…
Graph Attention Recurrent Neural Networks for Correlated Time Series Forecasting -- Full version
Razvan-Gabriel Cirstea, Chenjuan Guo, Bin Yang
We consider a setting where multiple entities inter-act with each other over time and the time-varying statuses of the entities are represented as multiple correlated time series.…
Distributed Reinforcement Learning for Flexible and Efficient UAV Swarm Control
Federico Venturini, Federico Mason, Francesco Pase +4
Over the past few years, the use of swarms of Unmanned Aerial Vehicles (UAVs) in monitoring and remote area surveillance applications has become widespread thanks to the price redu…
Towards Deepening Graph Neural Networks: A GNTK-based Optimization Perspective
Wei Huang, Yayong Li, Weitao Du +4
Graph convolutional networks (GCNs) and their variants have achieved great success in dealing with graph-structured data. Nevertheless, it is well known that deep GCNs suffer from…
Exploring Adversarial Robustness of Deep Metric Learning
Thomas Kobber Panum, Zi Wang, Pengyu Kan +2
Deep Metric Learning (DML), a widely-used technique, involves learning a distance metric between pairs of samples. DML uses deep neural architectures to learn semantic embeddings o…
A Review of Graph Neural Networks and Their Applications in Power Systems
Wenlong Liao, Birgitte Bak-Jensen, Jayakrishnan Radhakrishna Pillai +2
Deep neural networks have revolutionized many machine learning tasks in power systems, ranging from pattern recognition to signal processing. The data in these tasks is typically r…