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Found 9 papers, 7 papers with code
Why Do We Need Weight Decay in Modern Deep Learning?
In this work, we highlight that the role of weight decay in modern deep learning is different from its regularization effect studied in classical learning theory.
Uncertainty estimation under model misspecification in neural network regression
Although neural networks are powerful function approximators, the underlying modelling assumptions ultimately define the likelihood and thus the hypothesis class they are parameterizing.
On out-of-distribution detection with Bayesian neural networks
In this paper, we question this assumption and show that proper Bayesian inference with function space priors induced by neural networks does not necessarily lead to good OOD detection.
Are Bayesian neural networks intrinsically good at out-of-distribution detection?
The need to avoid confident predictions on unfamiliar data has sparked interest in out-of-distribution (OOD) detection.
Repulsive Deep Ensembles are Bayesian
Deep ensembles have recently gained popularity in the deep learning community for their conceptual simplicity and efficiency.
On Stein Variational Neural Network Ensembles
Ensembles of deep neural networks have achieved great success recently, but they do not offer a proper Bayesian justification.
Posterior Meta-Replay for Continual Learning
We offer a practical deep learning implementation of our framework based on probabilistic task-conditioned hypernetworks, an approach we term posterior meta-replay.
Annealed Stein Variational Gradient Descent
Particle based optimization algorithms have recently been developed as sampling methods that iteratively update a set of particles to approximate a target distribution.
Learning the Ising Model with Generative Neural Networks
We also find that convolutional layers in VAEs are important to model spin correlations whereas RBMs achieve similar or even better performances without convolutional filters.
