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Enhanced GPIS Learning Based on Local and Global Focus Areas
Implicit surface learning is one of the most widely used methods for 3D surface reconstruction from raw point cloud data. Current …
Zuka Murvanidze
,
Marc P. Deisenroth
,
Yasemin Bekiroglu
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The Graph Cut Kernel for Ranked Data
Many algorithms for ranked data become computationally intractable as the number of objects grows due to the complex geometric …
Michelangelo Conserva
,
Marc P. Deisenroth
,
K. S. Sesh Kumar
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Iterative State Estimation in Non-linear Dynamical Systems Using Approximate Expectation Propagation
Bayesian inference in non-linear dynamical systems seeks to find good posterior approximations of a latent state given a sequence of …
Sanket Kamthe
,
So Takao
,
Shakir Mohamed
,
Marc P. Deisenroth
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Cauchy-Schwarz Regularized Autoencoder
Recent work in unsupervised learning has focused on efficient inference and learning in latent variables models. Training these models …
Linh Tran
,
Maja Pantic
,
Marc P. Deisenroth
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Pathwise Conditioning of Gaussian Processes
As Gaussian processes are used to answer increasingly complex questions, analytic solutions become scarcer and scarcer. Monte Carlo …
James T. Wilson
,
Viacheslav Borovitskiy
,
Alexander Terenin
,
Peter Mostowsky
,
Marc P. Deisenroth
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GPflux: A Library for Deep Gaussian Processes
We introduce GPflux, a Python library for Bayesian deep learning with a strong emphasis on deep Gaussian processes (DGPs). Implementing …
Vincent Dutordoir
,
Hugh Salimbeni
,
Eric Hambro
,
John McLeod
,
Felix Leibfried
,
Artem Artemev
,
Mark van der Wilk
,
James Hensman
,
Marc P. Deisenroth
,
ST John
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Simultaneous Tactile Exploration and Grasp Refinement for Unknown Objects
Christiana de Farias
,
Naresh Marturi
,
Rustam Stolkin
,
Yasemin Bekiroglu
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High-Dimensional Bayesian Optimization with Manifold Gaussian Processes
Bayesian optimization (BO) is a powerful approach for seeking the global optimum of expensive black-box functions and has proven …
Riccardo Moriconi
,
Marc Deisenroth
,
K. S. Sesh Kumar
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A Foliated View of Transfer Learning
Transfer learning considers a learning process where a new task is solved by transferring relevant knowledge from known solutions to …
Janith Petangoda
,
Nick A. M. Monk
,
Marc Deisenroth
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Estimating Barycenters of Measures in High Dimensions
Barycentric averaging is a principled way of summarizing populations of measures. Existing algorithms for estimating barycenters …
Samuel Cohen
,
Michael Arbel
,
Marc Deisenroth
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