Search Results for author:
Found 22 papers, 7 papers with code
Neural machine translation for automated feedback on children's early-stage writing
In this work, we address the problem of assessing and constructing feedback for early-stage writing automatically using machine learning.
Polygonizer: An auto-regressive building delineator
In geospatial planning, it is often essential to represent objects in a vectorized format, as this format easily translates to downstream tasks such as web development, graphics, or design.
Learning to Generate 3D Representations of Building Roofs Using Single-View Aerial Imagery
We present a novel pipeline for learning the conditional distribution of a building roof mesh given pixels from an aerial image, under the assumption that roof geometry follows a set of regular patterns.
On the role of Model Uncertainties in Bayesian Optimization
In this work, we provide an extensive study of the relationship between the BO performance (regret) and uncertainty calibration for popular surrogate models and compare them across both synthetic and real-world experiments.
SolarDK: A high-resolution urban solar panel image classification and localization dataset
The body of research on classification of solar panel arrays from aerial imagery is increasing, yet there are still not many public benchmark datasets.
Robust, Automated, and Accurate Black-box Variational Inference
RAABBVI adaptively decreases the learning rate by detecting convergence of the fixed--learning-rate iterates, then estimates the symmetrized Kullback--Leiber (KL) divergence between the current variational approximation and the optimal one.
Challenges for BBVI with Normalizing Flows
Current black-box variational inference (BBVI) methods require the user to make numerous design choices---such as the selection of variational objective and approximating family---yet there is little principled guidance on how to do so.
Challenges and Opportunities in High Dimensional Variational Inference
Our framework and supporting experiments help to distinguish between the behavior of BBVI methods for approximating low-dimensional versus moderate-to-high-dimensional posteriors.
Challenges and Opportunities in High-dimensional Variational Inference
Our framework and supporting experiments help to distinguish between the behavior of BBVI methods for approximating low-dimensional versus moderate-to-high-dimensional posteriors.
Robust, Accurate Stochastic Optimization for Variational Inference
We consider the problem of fitting variational posterior approximations using stochastic optimization methods.
State Space Expectation Propagation: Efficient Inference Schemes for Temporal Gaussian Processes
EP provides some benefits over the traditional methods via introduction of the so-called cavity distribution, and we combine these benefits with the computational efficiency of linearisation, providing extensive empirical analysis demonstrating the efficacy of various algorithms under this unifying framework.
Preferential Batch Bayesian Optimization
This direction has been mainly driven by the use of BO in machine learning hyper-parameter configuration problems.
Uncertainty-aware Sensitivity Analysis Using Rényi Divergences
For nonlinear supervised learning models, assessing the importance of predictor variables or their interactions is not straightforward because it can vary in the domain of the variables.
Global Approximate Inference via Local Linearisation for Temporal Gaussian Processes
The extended Kalman filter (EKF) is a classical signal processing algorithm which performs efficient approximate Bayesian inference in non-conjugate models by linearising the local measurement function, avoiding the need to compute intractable integrals when calculating the posterior.
Bayesian leave-one-out cross-validation for large data
Model inference, such as model comparison, model checking, and model selection, is an important part of model development.
End-to-End Probabilistic Inference for Nonstationary Audio Analysis
A typical audio signal processing pipeline includes multiple disjoint analysis stages, including calculation of a time-frequency representation followed by spectrogram-based feature analysis.
Unifying Probabilistic Models for Time-Frequency Analysis
In audio signal processing, probabilistic time-frequency models have many benefits over their non-probabilistic counterparts.
Variable selection for Gaussian processes via sensitivity analysis of the posterior predictive distribution
Variable selection for Gaussian process models is often done using automatic relevance determination, which uses the inverse length-scale parameter of each input variable as a proxy for variable relevance.
EEG source imaging assists decoding in a face recognition task
In this work we explore the generality of Edelman et al. hypothesis by considering decoding of face recognition.
Neurons and Cognition
Correcting boundary over-exploration deficiencies in Bayesian optimization with virtual derivative sign observations
Bayesian optimization (BO) is a global optimization strategy designed to find the minimum of an expensive black-box function, typically defined on a compact subset of $\mathcal{R}^d$, by using a Gaussian process (GP) as a surrogate model for the objective.
Bayesian inference for spatio-temporal spike-and-slab priors
In this work, we address the problem of solving a series of underdetermined linear inverse problems subject to a sparsity constraint.
Spatio-temporal Spike and Slab Priors for Multiple Measurement Vector Problems
We are interested in solving the multiple measurement vector (MMV) problem for instances, where the underlying sparsity pattern exhibit spatio-temporal structure motivated by the electroencephalogram (EEG) source localization problem.
