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Found 8 papers, 2 papers with code
Congruent Learning for Self-Regulated Federated Learning in 6G
Existing techniques tackle overfitting via regularization heuristics that rely on hyperparameter tuning and as such presume availability of representative validation data.
Variational Elliptical Processes
We present elliptical processes, a family of non-parametric probabilistic models that subsume Gaussian processes and Student's t processes.
The Elliptical Processes: a Family of Fat-tailed Stochastic Processes
We present the elliptical processes -- a family of non-parametric probabilistic models that subsumes the Gaussian process and the Student-t process.
Matrix Multilayer Perceptron
Models that output a vector of responses given some inputs, in the form of a conditional mean vector, are at the core of machine learning.
The NIGENS General Sound Events Database
Computational auditory scene analysis is gaining interest in the last years.
On the Convergence of Extended Variational Inference for Non-Gaussian Statistical Models
Recently, an improved framework, namely the extended variational inference (EVI), has been introduced and applied to derive analytically tractable solution by employing lower-bound approximation to the variational objective function.
Constructing the Matrix Multilayer Perceptron and its Application to the VAE
However, in certain scenarios we are interested in learning structured parameters (predictions) in the form of symmetric positive definite matrices.
Conditionally Independent Multiresolution Gaussian Processes
This in turn results in models which are prone to overfitting in the sense of excessive sensitivity to the chosen resolution, and predictions which are non-smooth at the boundaries.
