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09/05/2023
Sparse Function-space Representation of Neural Networks
Deep neural networks (NNs) are known to lack uncertainty estimates and s...
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06/07/2023
Improving Hyperparameter Learning under Approximate Inference in Gaussian Process Models
Approximate inference in Gaussian process (GP) models with non-conjugate...
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06/06/2023
Rao-Blackwellized Particle Smoothing for Simultaneous Localization and Mapping
Simultaneous localization and mapping (SLAM) is the task of building a m...
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06/06/2023
Memory-Based Dual Gaussian Processes for Sequential Learning
Sequential learning with Gaussian processes (GPs) is challenging when ac...
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06/03/2023
Variational Gaussian Process Diffusion Processes
Diffusion processes are a class of stochastic differential equations (SD...
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02/13/2023
Fixing Overconfidence in Dynamic Neural Networks
Dynamic neural networks are a recent technique that promises a remedy fo...
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01/31/2023
Transport with Support: Data-Conditional Diffusion Bridges
The dynamic Schrödinger bridge problem provides an appealing setting for...
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12/27/2022
MixupE: Understanding and Improving Mixup from Directional Derivative Perspective
Mixup is a popular data augmentation technique for training deep neural ...
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11/11/2022
Towards Improved Learning in Gaussian Processes: The Best of Two Worlds
Gaussian process training decomposes into inference of the (approximate)...
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11/02/2022
Fantasizing with Dual GPs in Bayesian Optimization and Active Learning
Gaussian processes (GPs) are the main surrogate functions used for seque...
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11/01/2022
Expansion of Visual Hints for Improved Generalization in Stereo Matching
We introduce visual hints expansion for guiding stereo matching to impro...
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08/16/2022
Uncertainty-guided Source-free Domain Adaptation
Source-free domain adaptation (SFDA) aims to adapt a classifier to an un...
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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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06/17/2022
Representational Multiplicity Should Be Exposed, Not Eliminated
It is prevalent and well-observed, but poorly understood, that two machi...
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05/27/2022
A Look at Improving Robustness in Visual-inertial SLAM by Moment Matching
The fusion of camera sensor and inertial data is a leading method for eg...
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11/16/2021
Non-separable Spatio-temporal Graph Kernels via SPDEs
Gaussian processes (GPs) provide a principled and direct approach for in...
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11/05/2021
Dual Parameterization of Sparse Variational Gaussian Processes
Sparse variational Gaussian process (SVGP) methods are a common choice f...
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11/02/2021
Spatio-Temporal Variational Gaussian Processes
We introduce a scalable approach to Gaussian process inference that comb...
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11/02/2021
Bayes-Newton Methods for Approximate Bayesian Inference with PSD Guarantees
We formulate natural gradient variational inference (VI), expectation pr...
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10/29/2021
Scalable Inference in SDEs by Direct Matching of the Fokker-Planck-Kolmogorov Equation
Simulation-based techniques such as variants of stochastic Runge-Kutta a...
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10/26/2021
Periodic Activation Functions Induce Stationarity
Neural network models are known to reinforce hidden data biases, making ...
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06/22/2021
HybVIO: Pushing the Limits of Real-time Visual-inertial Odometry
We present HybVIO, a novel hybrid approach for combining filtering-based...
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06/18/2021
Combining Pseudo-Point and State Space Approximations for Sum-Separable Gaussian Processes
Gaussian processes (GPs) are important probabilistic tools for inference...
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03/19/2021
Sparse Algorithms for Markovian Gaussian Processes
Approximate Bayesian inference methods that scale to very large datasets...
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01/05/2021
Novel View Synthesis via Depth-guided Skip Connections
We introduce a principled approach for synthesizing new views of a scene...
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11/05/2020
RealAnt: An Open-Source Low-Cost Quadruped for Research in Real-World Reinforcement Learning
Current robot platforms available for research are either very expensive...
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10/19/2020
Stationary Activations for Uncertainty Calibration in Deep Learning
We introduce a new family of non-linear neural network activation functi...
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10/18/2020
Movement-induced Priors for Deep Stereo
We propose a method for fusing stereo disparity estimation with movement...
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07/12/2020
State Space Expectation Propagation: Efficient Inference Schemes for Temporal Gaussian Processes
We formulate approximate Bayesian inference in non-conjugate temporal an...
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07/09/2020
Fast Variational Learning in State-Space Gaussian Process Models
Gaussian process (GP) regression with 1D inputs can often be performed i...
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06/24/2020
Movement Tracking by Optical Flow Assisted Inertial Navigation
Robust and accurate six degree-of-freedom tracking on portable devices r...
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06/22/2020
Deep Residual Mixture Models
We propose Deep Residual Mixture Models (DRMMs) which share the many des...
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04/23/2020
Practical Hilbert space approximate Bayesian Gaussian processes for probabilistic programming
Gaussian processes are powerful non-parametric probabilistic models for ...
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12/21/2019
Deep Automodulators
We introduce a novel autoencoder model that deviates from traditional au...
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12/06/2019
Gaussian Process Priors for View-Aware Inference
We derive a principled framework for encoding prior knowledge of informa...
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11/14/2019
Scalable Exact Inference in Multi-Output Gaussian Processes
Multi-output Gaussian processes (MOGPs) leverage the flexibility and int...
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06/02/2019
Iterative Path Reconstruction for Large-Scale Inertial Navigation on Smartphones
Modern smartphones have all the sensing capabilities required for accura...
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04/12/2019
Multi-View Stereo by Temporal Nonparametric Fusion
We propose a novel idea for depth estimation from unstructured multi-vie...
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04/12/2019
Towards Photographic Image Manipulation with Balanced Growing of Generative Autoencoders
We build on recent advances in progressively growing generative autoenco...
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04/10/2019
Know Your Boundaries: Constraining Gaussian Processes by Variational Harmonic Features
Gaussian processes (GPs) provide a powerful framework for extrapolation,...
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02/06/2019
Unstructured Multi-View Depth Estimation Using Mask-Based Multiplane Representation
This paper presents a novel method, MaskMVS, to solve depth estimation f...
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01/31/2019
End-to-End Probabilistic Inference for Nonstationary Audio Analysis
A typical audio signal processing pipeline includes multiple disjoint an...
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11/15/2018
Infinite-Horizon Gaussian Processes
Gaussian processes provide a flexible framework for forecasting, removin...
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11/06/2018
Unifying Probabilistic Models for Time-Frequency Analysis
In audio signal processing, probabilistic time-frequency models have man...
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08/10/2018
Deep Learning Based Speed Estimation for Constraining Strapdown Inertial Navigation on Smartphones
Strapdown inertial navigation systems are sensitive to the quality of th...
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07/25/2018
ADVIO: An authentic dataset for visual-inertial odometry
The lack of realistic and open benchmarking datasets for pedestrian visu...
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07/09/2018
Pioneer Networks: Progressively Growing Generative Autoencoder
We introduce a novel generative autoencoder network model that learns to...
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05/31/2018
Robust Gyroscope-Aided Camera Self-Calibration
Camera calibration for estimating the intrinsic parameters and lens dist...
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04/05/2018
Scalable Magnetic Field SLAM in 3D Using Gaussian Process Maps
We present a method for scalable and fully 3D magnetic field simultaneou...
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02/14/2018