Christoph Lippert
14 papers · 2011–2025 · 7 conferences · across top CS/AI conferences
Achievements
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π Conference Polyglot (7) πΊοΈ Taxonomy Completionist (10) π§ Keyword Pioneer π Interdisciplinary Bridge π Academic Marathon (14)
π§
Keyword Pioneer
π
Conference Polyglot
(7)
π
Academic Marathon
(14)
π
Trend Setter
π
Century Club
(14)
ποΈ
Keyword Collector
(58)
Conferences
NIPS (4)
MIDL (3)
CVPR (2)
ICLR (2)
AISTATS (1)
ICML (1)
UAI (1)
Top co-authors
Keywords
representation learning
(3)
medical imaging
(3)
bayesian inference
(3)
image classification
(2)
variational inference
(2)
self-supervised learning
(2)
kronecker product
(2)
gaussian process
(2)
multi-task learning
(1)
density estimation
(1)
vision transformer
(1)
uncertainty quantification
(1)
object detection
(1)
transfer learning
(1)
semantic segmentation
(1)
laplace approximation
(1)
instance segmentation
(1)
multimodal learning
(1)
probabilistic modeling
(1)
contrastive learning
(1)
Papers
Token Cropr: Faster ViTs for Quite a Few Tasks
CVPR 2025
Heterogeneous Medical Data Integration with Multi-Source StyleGAN
MIDL 2024
Kernelised Normalising Flows
ICLR 2024
Evaluating Age-Related Anatomical Consistency in Synthetic Brain MRI against Real-World Alzheimerβs Disease Data.
MIDL 2024
Iterative Patch Selection for High-Resolution Image Recognition
ICLR 2023
Training Normalizing Flows from Dependent Data
ICML 2023
Laplace approximated Gaussian process state-space models
UAI 2022
ContIG: Self-Supervised Multimodal Contrastive Learning for Medical Imaging With Genetics
CVPR 2022
Interpretable and Interactive Deep Multiple Instance Learning for Dental Caries Classification in Bitewing X-rays
MIDL 2022
3D Self-Supervised Methods for Medical Imaging
NIPS 2020
Beyond the Mean-Field: Structured Deep Gaussian Processes Improve the Predictive Uncertainties
NIPS 2020
Two-sample Testing Using Deep Learning
AISTATS 2020
It is all in the noise: Efficient multi-task Gaussian process inference with structured residuals
NIPS 2013
Efficient inference in matrix-variate Gaussian models with \iid observation noise
NIPS 2011