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Found 6 papers, 5 papers with code
Disentangling the Predictive Variance of Deep Ensembles through the Neural Tangent Kernel
Identifying unfamiliar inputs, also known as out-of-distribution (OOD) detection, is a crucial property of any decision making process.
Meta-Learning via Classifier(-free) Diffusion Guidance
Our methods repurpose the popular generative image synthesis techniques of natural language guidance and diffusion models to generate neural network weights adapted for tasks.
The least-control principle for local learning at equilibrium
As special cases, they include models of great current interest in both neuroscience and machine learning, such as deep neural networks, equilibrium recurrent neural networks, deep equilibrium models, or meta-learning.
Learning where to learn: Gradient sparsity in meta and continual learning
We find that patterned sparsity emerges from this process, with the pattern of sparsity varying on a problem-by-problem basis.
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 #67 on
Image Classification
on CIFAR-100
(using extra training data)
