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Found 4 papers, 2 papers with code
Bayesian sparsification for deep neural networks with Bayesian model reduction
Bayesian sparsification for deep learning emerges as a crucial approach, facilitating the design of models that are both computationally efficient and competitive in terms of performance across various deep learning applications.
Strategy Synthesis in Markov Decision Processes Under Limited Sampling Access
A central task in control theory, artificial intelligence, and formal methods is to synthesize reward-maximizing strategies for agents that operate in partially unknown environments.
An empirical evaluation of active inference in multi-armed bandits
This comparison is done on two types of bandit problems: a stationary and a dynamic switching bandit.
Neuronal Sequence Models for Bayesian Online Inference
While probabilistic inference about ongoing sequences is a useful computational model for both the analysis of neuroscientific data and a wide range of problems in artificial recognition and motor control, research on the subject is relatively scarce and distributed over different fields in the neurosciences.
