Search Results for author: Hippolyt Ritter

Found 10 papers, 4 papers with code

Black-box Coreset Variational Inference

1 code implementation4 Nov 2022 Dionysis Manousakas, Hippolyt Ritter, Theofanis Karaletsos

Recent advances in coreset methods have shown that a selection of representative datapoints can replace massive volumes of data for Bayesian inference, preserving the relevant statistical information and significantly accelerating subsequent downstream tasks.

Bayesian Inference Data Summarization +2

TyXe: Pyro-based Bayesian neural nets for Pytorch

1 code implementation1 Oct 2021 Hippolyt Ritter, Theofanis Karaletsos

We introduce TyXe, a Bayesian neural network library built on top of Pytorch and Pyro.

Continual Learning Image Classification

Sparse Uncertainty Representation in Deep Learning with Inducing Weights

no code implementations NeurIPS 2021 Hippolyt Ritter, Martin Kukla, Cheng Zhang, Yingzhen Li

Bayesian neural networks and deep ensembles represent two modern paradigms of uncertainty quantification in deep learning.

Uncertainty Quantification

Bayesian Online Meta-Learning

no code implementations28 Sep 2020 Pauching Yap, Hippolyt Ritter, David Barber

This work introduces a Bayesian online meta-learning framework to tackle the catastrophic forgetting and the sequential few-shot tasks problems.

Classification Meta-Learning +1

Addressing Catastrophic Forgetting in Few-Shot Problems

1 code implementation30 Apr 2020 Pauching Yap, Hippolyt Ritter, David Barber

We demonstrate that the popular gradient-based model-agnostic meta-learning algorithm (MAML) indeed suffers from catastrophic forgetting and introduce a Bayesian online meta-learning framework that tackles this problem.

Classification General Classification +2

Gaussian Mean Field Regularizes by Limiting Learned Information

no code implementations12 Feb 2019 Julius Kunze, Louis Kirsch, Hippolyt Ritter, David Barber

Variational inference with a factorized Gaussian posterior estimate is a widely used approach for learning parameters and hidden variables.

Variational Inference

Noisy Information Bottlenecks for Generalization

no code implementations27 Sep 2018 Julius Kunze, Louis Kirsch, Hippolyt Ritter, David Barber

We propose Noisy Information Bottlenecks (NIB) to limit mutual information between learned parameters and the data through noise.

Online Structured Laplace Approximations For Overcoming Catastrophic Forgetting

no code implementations NeurIPS 2018 Hippolyt Ritter, Aleksandar Botev, David Barber

In order to make our method scalable, we leverage recent block-diagonal Kronecker factored approximations to the curvature.

Permuted-MNIST

A Scalable Laplace Approximation for Neural Networks

1 code implementation ICLR 2018 Hippolyt Ritter, Aleksandar Botev, David Barber

Pytorch implementations of Bayes By Backprop, MC Dropout, SGLD, the Local Reparametrization Trick, KF-Laplace and more

Bayesian Inference

Practical Gauss-Newton Optimisation for Deep Learning

no code implementations ICML 2017 Aleksandar Botev, Hippolyt Ritter, David Barber

We present an efficient block-diagonal ap- proximation to the Gauss-Newton matrix for feedforward neural networks.

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