Proceedings of the Sixteenth Conference on Uncertainty in Artificial Intelligence (2000)
arXiv:1304.3842
Abstract
This is the Proceedings of the Sixteenth Conference on Uncertainty in Artificial Intelligence, which was held in San Francisco, CA, June 30 - July 3, 2000
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- A physics-aware, probabilistic machine learning framework for coarse-graining high-dimensional systems in the Small Data regime
- Incorporating physical constraints in a deep probabilistic machine learning framework for coarse-graining dynamical systems
- Sparse Polynomial Chaos expansions using Variational Relevance Vector Machines
- Combining Particle MCMC with Rao-Blackwellized Monte Carlo Data Association for Parameter Estimation in Multiple Target Tracking
- Random projections in gravitational wave searches of compact binaries
- Coordination problems on networks revisited: statics and dynamics