24 citations · 54 across the 17 of their papers we have counts for
3 papers · 1 filter
Disentangling the Predictive Variance of Deep Ensembles through the Neural Tangent Kernel
Seijin Kobayashi, Pau Vilimelis Aceituno, Johannes von Oswald
Identifying unfamiliar inputs, also known as out-of-distribution (OOD) detection, is a crucial property of any decision making process. A simple and empirically validated technique…
Meta-Learning via Classifier(-free) Diffusion Guidance
Elvis Nava, Seijin Kobayashi, Yifei Yin +2
We introduce meta-learning algorithms that perform zero-shot weight-space adaptation of neural network models to unseen tasks. Our methods repurpose the popular generative image sy…
The least-control principle for local learning at equilibrium
Alexander Meulemans, Nicolas Zucchet, Seijin Kobayashi +2
Equilibrium systems are a powerful way to express neural computations. As special cases, they include models of great current interest in both neuroscience and machine learning, su…