TP1b.4

State and Dynamics Estimation with the Kalman-Langevin Filter

Martin Sevilla, Nicolas Zilberstein, Rice University, United States; Benjamin Cox, Sara Perez-Vieites, Victor Elvira, University of Edinburgh, United Kingdom; Santiago Segarra, Rice University, United States
Session:
TP1b: Machine Learning Meets Monte Carlo (Invited) Lecture
Track:
Adaptive Systems, Machine Learning, Data Analytics
Location:
Evergreen
Presentation Time:
Tue, 31 Oct, 16:45 - 17:10 PT (UTC -7)
Session Chair:
Yunpeng Li, University of Surrey
Presentation
Discussion
Resources
No resources available.
Session TP1b
TP1b.1: Learning Differentiable Particle Filter on the Fly
Jiaxi Li, Xiongjie Chen, Yunpeng Li, University of Surrey, United Kingdom
TP1b.2: On the Impact of Sampling on Deep Sequential State Estimation
Helena Calatrava, Northeastern University, United States; Ricardo Borsoi, University of Lorraine, CNRS, France; Tales Imbiriba, Pau Closas, Northeastern University, United States
TP1b.3: Fusion of Gaussian Processes Predictions with Monte Carlo Sampling
Marzieh Ajirak, Daniel Waxman, Fernando Llorente, Petar Djuric, Stony Brook University, United States
TP1b.4: State and Dynamics Estimation with the Kalman-Langevin Filter
Martin Sevilla, Nicolas Zilberstein, Rice University, United States; Benjamin Cox, Sara Perez-Vieites, Victor Elvira, University of Edinburgh, United Kingdom; Santiago Segarra, Rice University, United States
Contacts