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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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04/17/2023
Refusion: Enabling Large-Size Realistic Image Restoration with Latent-Space Diffusion Models
This work aims to improve the applicability of diffusion models in reali...
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04/02/2023
On the trade-off between event-based and periodic state estimation under bandwidth constraints
Event-based methods carefully select when to transmit information to ena...
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03/09/2023
Invertible Kernel PCA with Random Fourier Features
Kernel principal component analysis (kPCA) is a widely studied method to...
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02/07/2023
How Reliable is Your Regression Model's Uncertainty Under Real-World Distribution Shifts?
Many important computer vision applications are naturally formulated as ...
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01/30/2023
Deep networks for system identification: a Survey
Deep learning is a topic of considerable current interest. The availabil...
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01/27/2023
Image Restoration with Mean-Reverting Stochastic Differential Equations
This paper presents a stochastic differential equation (SDE) approach fo...
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12/21/2022
ECG-Based Electrolyte Prediction: Evaluating Regression and Probabilistic Methods
Objective: Imbalances of the electrolyte concentration levels in the bod...
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10/26/2022
Nonlinear System Identification: Learning while respecting physical models using a sequential Monte Carlo method
Identification of nonlinear systems is a challenging problem. Physical k...
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05/25/2022
Surprises in adversarially-trained linear regression
State-of-the-art machine learning models can be vulnerable to very small...
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05/12/2022
Probabilistic Estimation of Chirp Instantaneous Frequency Using Gaussian Processes
We present a probabilistic approach for estimating chirp signal and its ...
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04/13/2022
Overparameterized Linear Regression under Adversarial Attacks
As machine learning models start to be used in critical applications, th...
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02/03/2022
Incorporating Sum Constraints into Multitask Gaussian Processes
Machine learning models can be improved by adapting them to respect exis...
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11/02/2021
Efficient Learning of the Parameters of Non-Linear Models using Differentiable Resampling in Particle Filters
It has been widely documented that the sampling and resampling steps in ...
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10/22/2021
Learning Proposals for Practical Energy-Based Regression
Energy-based models (EBMs) have experienced a resurgence within machine ...
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04/28/2021
Learning deep autoregressive models for hierarchical data
We propose a model for hierarchical structured data as an extension to t...
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03/01/2021
Latent linear dynamics in spatiotemporal medical data
Spatiotemporal imaging is common in medical imaging, with applications i...
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02/15/2021
How Convolutional Neural Networks Deal with Aliasing
The convolutional neural network (CNN) remains an essential tool in solv...
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12/11/2020
Beyond Occam's Razor in System Identification: Double-Descent when Modeling Dynamics
System identification aims to build models of dynamical systems from dat...
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12/08/2020
Accurate 3D Object Detection using Energy-Based Models
Accurate 3D object detection (3DOD) is crucial for safe navigation of co...
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12/08/2020
Deep Energy-Based NARX Models
This paper is directed towards the problem of learning nonlinear ARX mod...
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05/04/2020
How to Train Your Energy-Based Model for Regression
Energy-based models (EBMs) have become increasingly popular within compu...
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03/31/2020
Deep State Space Models for Nonlinear System Identification
An actively evolving model class for generative temporal models develope...
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03/24/2020
Registration by tracking for sequential 2D MRI
Our anatomy is in constant motion. With modern MR imaging it is possible...
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02/07/2020
Constructing a variational family for nonlinear state-space models
We consider the problem of maximum likelihood parameter estimation for n...
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12/31/2019
Optimistic robust linear quadratic dual control
Recent work by Mania et al. has proved that certainty equivalent control...
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10/21/2019
Particle filter with rejection control and unbiased estimator of the marginal likelihood
We consider the combined use of resampling and partial rejection control...
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10/01/2019
A Fast and Robust Algorithm for Orientation Estimation using Inertial Sensors
We present a novel algorithm for online, real-time orientation estimatio...
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09/26/2019
DCTD: Deep Conditional Target Densities for Accurate Regression
While deep learning-based classification is generally addressed using st...
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09/04/2019
Deep kernel learning for integral measurements
Deep kernel learning refers to a Gaussian process that incorporates neur...
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09/04/2019
Deep Convolutional Networks in System Identification
Recent developments within deep learning are relevant for nonlinear syst...
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07/10/2019
Probabilistic programming for birth-death models of evolution using an alive particle filter with delayed sampling
We consider probabilistic programming for birth-death models of evolutio...
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06/20/2019
The trade-off between long-term memory and smoothness for recurrent networks
Training recurrent neural networks (RNNs) that possess long-term memory ...
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06/04/2019
Evaluating Scalable Bayesian Deep Learning Methods for Robust Computer Vision
While Deep Neural Networks (DNNs) have become the go-to approach in comp...
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06/04/2019
Robust exploration in linear quadratic reinforcement learning
This paper concerns the problem of learning control policies for an unkn...
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05/02/2019
On the Smoothness of Nonlinear System Identification
New light is shed onto optimization problems resulting from prediction e...
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04/02/2019
Automatic Diagnosis of the Short-Duration 12-Lead ECG using a Deep Neural Network: the CODE Study
We present a Deep Neural Network (DNN) model for predicting electrocardi...
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03/12/2019
Elements of Sequential Monte Carlo
A core problem in statistics and probabilistic machine learning is to co...
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03/06/2019
Nonlinear input design as optimal control of a Hamiltonian system
We propose an input design method for a general class of parametric prob...
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02/19/2019
Evaluating model calibration in classification
Probabilistic classifiers output a probability distribution on target cl...
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02/04/2019
Constructing the Matrix Multilayer Perceptron and its Application to the VAE
Like most learning algorithms, the multilayer perceptrons (MLP) is desig...
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11/28/2018
Automatic Diagnosis of Short-Duration 12-Lead ECG using a Deep Convolutional Network
We present a model for predicting electrocardiogram (ECG) abnormalities ...
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10/02/2018
Automated learning with a probabilistic programming language: Birch
This work offers a broad perspective on probabilistic modeling and infer...
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09/11/2018
Probabilistic approach to limited-data computed tomography reconstruction
We consider the problem of reconstructing the internal structure of an o...
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08/17/2018
Data Consistency Approach to Model Validation
In scientific inference problems, the underlying statistical modeling as...
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06/01/2018
Learning convex bounds for linear quadratic control policy synthesis
Learning to make decisions from observed data in dynamic environments re...
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02/25/2018
Conditionally Independent Multiresolution Gaussian Processes
We propose a multiresolution Gaussian process (GP) model which assumes c...
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02/09/2018
Learning Localized Spatio-Temporal Models From Streaming Data
We address the problem of predicting spatio-temporal processes with temp...
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01/25/2018
Data-Driven Impulse Response Regularization via Deep Learning
We consider the problem of impulse response estimation for stable linear...
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12/07/2017
