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Found 7 papers, 7 papers with code
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.
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.
Neural networks with late-phase weights
The largely successful method of training neural networks is to learn their weights using some variant of stochastic gradient descent (SGD).
Ranked #70 on
Image Classification
on CIFAR-100
(using extra training data)
Continual Learning in Recurrent Neural Networks
Here, we provide the first comprehensive evaluation of established CL methods on a variety of sequential data benchmarks.
Continual learning with hypernetworks
Artificial neural networks suffer from catastrophic forgetting when they are sequentially trained on multiple tasks.
Ranked #4 on
Continual Learning
on F-CelebA (10 tasks)
