Search Results for author: Seijin Kobayashi

Found 6 papers, 5 papers with code

Disentangling the Predictive Variance of Deep Ensembles through the Neural Tangent Kernel

no code implementations18 Oct 2022 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.

Decision Making Inductive Bias +1

Meta-Learning via Classifier(-free) Diffusion Guidance

1 code implementation17 Oct 2022 Elvis Nava, Seijin Kobayashi, Yifei Yin, Robert K. Katzschmann, Benjamin F. Grewe

Our methods repurpose the popular generative image synthesis techniques of natural language guidance and diffusion models to generate neural network weights adapted for tasks.

Few-Shot Learning Image Generation +2

The least-control principle for local learning at equilibrium

1 code implementation4 Jul 2022 Alexander Meulemans, Nicolas Zucchet, Seijin Kobayashi, Johannes von Oswald, João Sacramento

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.

BIG-bench Machine Learning Meta-Learning

Posterior Meta-Replay for Continual Learning

3 code implementations NeurIPS 2021 Christian Henning, Maria R. Cervera, Francesco D'Angelo, Johannes von Oswald, Regina Traber, Benjamin Ehret, Seijin Kobayashi, Benjamin F. Grewe, João Sacramento

We offer a practical deep learning implementation of our framework based on probabilistic task-conditioned hypernetworks, an approach we term posterior meta-replay.

Continual Learning

Neural networks with late-phase weights

2 code implementations ICLR 2021 Johannes von Oswald, Seijin Kobayashi, Alexander Meulemans, Christian Henning, Benjamin F. Grewe, João Sacramento

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)

Image Classification

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