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Found 7 papers, 0 papers with code
No Compromise in Solution Quality: Speeding Up Belief-dependent Continuous POMDPs via Adaptive Multilevel Simplification
Using the general theoretical results, we present three algorithms to accelerate continuous POMDP online planning with belief-dependent rewards.
Simplified Continuous High Dimensional Belief Space Planning with Adaptive Probabilistic Belief-dependent Constraints
Moreover, using our proposed framework, we contribute an adaptive method to find a maximal feasible return (e. g., information gain) in terms of Value at Risk for the candidate action sequence with substantial acceleration.
Risk Aware Adaptive Belief-dependent Probabilistically Constrained Continuous POMDP Planning
In addition, with an arbitrary confidence parameter, we did not find any analogs to our approach.
Simplified Belief-Dependent Reward MCTS Planning with Guaranteed Tree Consistency
In particular, we present Simplified Information-Theoretic Particle Filter Tree (SITH-PFT), a novel variant to the MCTS algorithm that considers information-theoretic rewards but avoids the need to calculate them completely.
Probabilistic Loss and its Online Characterization for Simplified Decision Making Under Uncertainty
On top of this extension, our key contribution is a novel framework to simplify decision making while assessing and controlling online the simplification's impact.
Topology of deep neural networks
We study how the topology of a data set $M = M_a \cup M_b \subseteq \mathbb{R}^d$, representing two classes $a$ and $b$ in a binary classification problem, changes as it passes through the layers of a well-trained neural network, i. e., with perfect accuracy on training set and near-zero generalization error ($\approx 0. 01\%$).
Revealing Common Statistical Behaviors in Heterogeneous Populations
In many areas of neuroscience and biological data analysis, it is desired to reveal common patterns among a group of subjects.
