Mark van der Wilk

According to our database1, Mark van der Wilk authored at least 21 papers between 2014 and 2020.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Other 

Links

On csauthors.net:

Bibliography

2020
Variational Orthogonal Features.
CoRR, 2020

Stochastic Segmentation Networks: Modelling Spatially Correlated Aleatoric Uncertainty.
CoRR, 2020

Revisiting the Train Loss: an Efficient Performance Estimator for Neural Architecture Search.
CoRR, 2020

On the Benefits of Invariance in Neural Networks.
CoRR, 2020

Capsule Networks - A Probabilistic Perspective.
CoRR, 2020

A Framework for Interdomain and Multioutput Gaussian Processes.
CoRR, 2020

Bayesian Image Classification with Deep Convolutional Gaussian Processes.
Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019
Translation Insensitivity for Deep Convolutional Gaussian Processes.
CoRR, 2019

Bayesian Layers: A Module for Neural Network Uncertainty.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Scalable Bayesian dynamic covariance modeling with variational Wishart and inverse Wishart processes.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Overcoming Mean-Field Approximations in Recurrent Gaussian Process Models.
Proceedings of the 36th International Conference on Machine Learning, 2019

Rates of Convergence for Sparse Variational Gaussian Process Regression.
Proceedings of the 36th International Conference on Machine Learning, 2019

Variational Gaussian Process Models without Matrix Inverses.
Proceedings of the Symposium on Advances in Approximate Bayesian Inference, 2019

2018
Non-Factorised Variational Inference in Dynamical Systems.
CoRR, 2018

Closed-form Inference and Prediction in Gaussian Process State-Space Models.
CoRR, 2018

Learning Invariances using the Marginal Likelihood.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

2017
GPflow: A Gaussian Process Library using TensorFlow.
J. Mach. Learn. Res., 2017

Convolutional Gaussian Processes.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Concrete Problems for Autonomous Vehicle Safety: Advantages of Bayesian Deep Learning.
Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, 2017

2016
Understanding Probabilistic Sparse Gaussian Process Approximations.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

2014
Distributed Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models.
Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014


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