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07/09/2023
Learning Space-Time Continuous Neural PDEs from Partially Observed States
We introduce a novel grid-independent model for learning partial differe...
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06/05/2023
Input gradient diversity for neural network ensembles
Deep Ensembles (DEs) demonstrate improved accuracy, calibration and robu...
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05/31/2023
AbODE: Ab Initio Antibody Design using Conjoined ODEs
Antibodies are Y-shaped proteins that neutralize pathogens and constitut...
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05/12/2023
Beyond invariant representation learning: linearly alignable latent spaces for efficient closed-form domain adaptation
Optimal transport (OT) is a powerful geometric tool used to compare and ...
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04/10/2023
Uncertainty-Aware Natural Language Inference with Stochastic Weight Averaging
This paper introduces Bayesian uncertainty modeling using Stochastic Wei...
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03/03/2023
Learning Energy Conserving Dynamics Efficiently with Hamiltonian Gaussian Processes
Hamiltonian mechanics is one of the cornerstones of natural sciences. Re...
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03/01/2023
Continuous-Time Functional Diffusion Processes
We introduce functional diffusion processes (FDPs), which generalize tra...
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10/12/2022
Modular Flows: Differential Molecular Generation
Generating new molecules is fundamental to advancing critical applicatio...
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10/07/2022
Latent Neural ODEs with Sparse Bayesian Multiple Shooting
Training dynamic models, such as neural ODEs, on long trajectories is a ...
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07/04/2022
Look beyond labels: Incorporating functional summary information in Bayesian neural networks
Bayesian deep learning offers a principled approach to train neural netw...
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06/21/2022
Generative Modelling With Inverse Heat Dissipation
While diffusion models have shown great success in image generation, the...
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06/06/2022
Tackling covariate shift with node-based Bayesian neural networks
Bayesian neural networks (BNNs) promise improved generalization under co...
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10/07/2021
De-randomizing MCMC dynamics with the diffusion Stein operator
Approximate Bayesian inference estimates descriptors of an intractable t...
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06/29/2021
Evolving-Graph Gaussian Processes
Graph Gaussian Processes (GGPs) provide a data-efficient solution on gra...
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06/21/2021
Bayesian inference of ODEs with Gaussian processes
Recent machine learning advances have proposed black-box estimation of u...
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05/25/2021
Affine Transport for Sim-to-Real Domain Adaptation
Sample-efficient domain adaptation is an open problem in robotics. In th...
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02/09/2021
Continuous-Time Model-Based Reinforcement Learning
Model-based reinforcement learning (MBRL) approaches rely on discrete-ti...
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11/02/2020
Sample-efficient reinforcement learning using deep Gaussian processes
Reinforcement learning provides a framework for learning to control whic...
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10/26/2020
Scalable Bayesian neural networks by layer-wise input augmentation
We introduce implicit Bayesian neural networks, a simple and scalable ap...
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10/19/2020
Bayesian Inference for Optimal Transport with Stochastic Cost
In machine learning and computer vision, optimal transport has had signi...
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06/18/2020
Likelihood-Free Inference with Deep Gaussian Processes
In recent years, surrogate models have been successfully used in likelih...
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06/16/2020
Learning continuous-time PDEs from sparse data with graph neural networks
The behavior of many dynamical systems follow complex, yet still unknown...
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03/06/2020
Rethinking Sparse Gaussian Processes: Bayesian Approaches to Inducing-Variable Approximations
Variational inference techniques based on inducing variables provide an ...
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05/27/2019
ODE^2VAE: Deep generative second order ODEs with Bayesian neural networks
We present Ordinary Differential Equation Variational Auto-Encoder (ODE^...
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05/23/2019
Learning spectrograms with convolutional spectral kernels
We introduce the convolutional spectral kernel (CSK), a novel family of ...
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11/27/2018
Neural Non-Stationary Spectral Kernel
Standard kernels such as Matérn or RBF kernels only encode simple monoto...
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10/10/2018
Harmonizable mixture kernels with variational Fourier features
The expressive power of Gaussian processes depends heavily on the choice...
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10/09/2018
Deep learning with differential Gaussian process flows
We propose a novel deep learning paradigm of differential flows that lea...
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10/06/2018
Deep convolutional Gaussian processes
We propose deep convolutional Gaussian processes, a deep Gaussian proces...
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07/16/2018
Learning Stochastic Differential Equations With Gaussian Processes Without Gradient Matching
We introduce a novel paradigm for learning non-parametric drift and diff...
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04/18/2018
Bayesian Metabolic Flux Analysis reveals intracellular flux couplings
Metabolic flux balance analyses are a standard tool in analysing metabol...
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03/13/2018
Variational zero-inflated Gaussian processes with sparse kernels
Zero-inflated datasets, which have an excess of zero outputs, are common...
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03/12/2018
Learning unknown ODE models with Gaussian processes
In conventional ODE modelling coefficients of an equation driving the sy...
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02/08/2018
mGPfusion: Predicting protein stability changes with Gaussian process kernel learning and data fusion
Proteins are commonly used by biochemical industry for numerous processe...
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05/24/2017
Non-Stationary Spectral Kernels
We propose non-stationary spectral kernels for Gaussian process regressi...
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02/27/2017
A Mutually-Dependent Hadamard Kernel for Modelling Latent Variable Couplings
We introduce a novel kernel that models input-dependent couplings across...
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08/18/2015
Non-Stationary Gaussian Process Regression with Hamiltonian Monte Carlo
We present a novel approach for fully non-stationary Gaussian process re...
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11/19/2014
