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Found 9 papers, 6 papers with code
A Bayesian Monte Carlo approach for predicting the spread of infectious diseases
In this paper, a simple yet interpretable, probabilistic model is proposed for the prediction of reported case counts of infectious diseases.
Cortical Spike Synchrony as a Measure of Input Familiarity
Spike synchrony, which occurs in various cortical areas in response to specific perception, action and memory tasks, has sparked a long-standing debate on the nature of temporal organization in cortex.
Neurons and Cognition
Reliability and comparability of human brain structural covariance networks
In terms of comparability, our results show substantial differences in the structural covariance matrix between data sets of age- and sex-matched healthy human adults.
Neurons and Cognition Quantitative Methods
Fast Concept Mapping: The Emergence of Human Abilities in Artificial Neural Networks when Learning Embodied and Self-Supervised
Following this, the representations learned through interaction with the world can be used to associate semantic concepts such as different types of doors.
Predicting epileptic seizures using nonnegative matrix factorization
The resulting method yields a computationally and conceptually simple, interpretable model of EEG signals of preictal and interictal states, which shows a good performance for the task of seizure prediction.
Bayesian hierarchical models can infer interpretable predictions of leaf area index from heterogeneous datasets
Environmental scientists often have to predict a complex phenomenon from a heterogeneous collection of datasets.
Adaptive Blending Units: Trainable Activation Functions for Deep Neural Networks
Since ABUs learn the shape, as well as the overall scaling of the activation function, we also analyze the effects of adaptive scaling in common activation functions.
WestDrive X LoopAR: An open-access virtual reality project in Unity for evaluating user interaction methods during TOR
The presented project contains all needed functionalities for realistic traffic behavior, cars, and pedestrians, as well as a large, open-source, scriptable, and modular VR environment.
Human-Computer Interaction
Learning Semantically Meaningful Representations Through Embodiment
How do humans acquire a meaningful understanding of the world with little to no supervision or semantic labels provided by the environment?
