Hippolyt Ritter

Publication Activity (10 Years)

Years Active: 2017-2023
Publications (10 Years): 15

Top Topics

Top Venues

Publications

Hang Wang, Sahar Karami, Ousmane Dia, Hippolyt Ritter, Ehsan Emamjomeh-Zadeh, Jiahui Chen, Zhen Xiang, David J. Miller, George Kesidis
Training Set Cleansing of Backdoor Poisoning by Self-Supervised Representation Learning. ICASSP (2023)
Dionysis Manousakas, Hippolyt Ritter, Theofanis Karaletsos
Black-box Coreset Variational Inference. CoRR (2022)
Hippolyt Ritter, Theofanis Karaletsos
TyXe: Pyro-based Bayesian neural nets for Pytorch. MLSys (2022)
Dionysis Manousakas, Hippolyt Ritter, Theofanis Karaletsos
Black-box coreset variational inference. NeurIPS (2022)
Hippolyt Ritter, Theofanis Karaletsos
TyXe: Pyro-based Bayesian neural nets for Pytorch. CoRR (2021)
Hippolyt Ritter, Martin Kukla, Cheng Zhang, Yingzhen Li
Sparse Uncertainty Representation in Deep Learning with Inducing Weights. NeurIPS (2021)
Hippolyt Ritter, Martin Kukla, Cheng Zhang, Yingzhen Li
Sparse Uncertainty Representation in Deep Learning with Inducing Weights. CoRR (2021)
Pau Ching Yap, Hippolyt Ritter, David Barber
Addressing Catastrophic Forgetting in Few-Shot Problems. ICML (2021)
Pau Ching Yap, Hippolyt Ritter, David Barber
Bayesian Online Meta-Learning with Laplace Approximation. CoRR (2020)
Julius Kunze, Louis Kirsch, Hippolyt Ritter, David Barber
Gaussian Mean Field Regularizes by Limiting Learned Information. CoRR (2019)
Julius Kunze, Louis Kirsch, Hippolyt Ritter, David Barber
Gaussian Mean Field Regularizes by Limiting Learned Information. Entropy 21 (8) (2019)
Hippolyt Ritter, Aleksandar Botev, David Barber
Online Structured Laplace Approximations For Overcoming Catastrophic Forgetting. CoRR (2018)
Hippolyt Ritter, Aleksandar Botev, David Barber
Online Structured Laplace Approximations for Overcoming Catastrophic Forgetting. NeurIPS (2018)
Hippolyt Ritter, Aleksandar Botev, David Barber
A Scalable Laplace Approximation for Neural Networks. ICLR (Poster) (2018)
Aleksandar Botev, Hippolyt Ritter, David Barber
Practical Gauss-Newton Optimisation for Deep Learning. ICML (2017)