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Found 4 papers, 1 papers with code
Reframing the Expected Free Energy: Four Formulations and a Unification
In the second setting, a justification of the root expected free energy definition is known, but this setting only accounts for two formulations, i. e., the risk over states plus ambiguity and entropy plus expected energy formulations.
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.
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.
Body movement to sound interface with vector autoregressive hierarchical hidden Markov models
Interfacing a kinetic action of a person to an action of a machine system is an important research topic in many application areas.
