Search Results for author:
Found 4 papers, 3 papers with code
Variational Learning is Effective for Large Deep Networks
We give extensive empirical evidence against the common belief that variational learning is ineffective for large neural networks.
The Memory Perturbation Equation: Understanding Model's Sensitivity to Data
Understanding model's sensitivity to its training data is crucial but can also be challenging and costly, especially during training.
Variational Hierarchical Mixtures for Probabilistic Learning of Inverse Dynamics
We derive two efficient variational inference techniques to learn these representations and highlight the advantages of hierarchical infinite local regression models, such as dealing with non-smooth functions, mitigating catastrophic forgetting, and enabling parameter sharing and fast predictions.
A Variational Infinite Mixture for Probabilistic Inverse Dynamics Learning
Probabilistic regression techniques in control and robotics applications have to fulfill different criteria of data-driven adaptability, computational efficiency, scalability to high dimensions, and the capacity to deal with different modalities in the data.
