78 citations · 253 across the 56 of their papers we have counts for
8 papers · 1 filter
SolarDK: A high-resolution urban solar panel image classification and localization dataset
Maxim Khomiakov, Julius Holbech Radzikowski, Carl Anton Schmidt +4
The body of research on classification of solar panel arrays from aerial imagery is increasing, yet there are still not many public benchmark datasets. This paper introduces two no…
Explainability as statistical inference
Hugo Henri Joseph Senetaire, Damien Garreau, Jes Frellsen +1
A wide variety of model explanation approaches have been proposed in recent years, all guided by very different rationales and heuristics. In this paper, we take a new route and ca…
Exploring Predictive Uncertainty and Calibration in NLP: A Study on the Impact of Method & Data Scarcity
Dennis Ulmer, Jes Frellsen, Christian Hardmeier
We investigate the problem of determining the predictive confidence (or, conversely, uncertainty) of a neural classifier through the lens of low-resource languages. By training mod…
deep-significance - Easy and Meaningful Statistical Significance Testing in the Age of Neural Networks
Dennis Ulmer, Christian Hardmeier, Jes Frellsen
A lot of Machine Learning (ML) and Deep Learning (DL) research is of an empirical nature. Nevertheless, statistical significance testing (SST) is still not widely used. This endang…
Model-agnostic out-of-distribution detection using combined statistical tests
Federico Bergamin, Pierre-Alexandre Mattei, Jakob D. Havtorn +5
We present simple methods for out-of-distribution detection using a trained generative model. These techniques, based on classical statistical tests, are model-agnostic in the sens…
Benchmarking Generative Latent Variable Models for Speech
Jakob D. Havtorn, Lasse Borgholt, Søren Hauberg +2
Stochastic latent variable models (LVMs) achieve state-of-the-art performance on natural image generation but are still inferior to deterministic models on speech. In this paper, w…
Adaptive Cholesky Gaussian Processes
Simon Bartels, Kristoffer Stensbo-Smidt, Pablo Moreno-Muñoz +3
We present a method to approximate Gaussian process regression models for large datasets by considering only a subset of the data. Our approach is novel in that the size of the sub…
Uphill Roads to Variational Tightness: Monotonicity and Monte Carlo Objectives
Pierre-Alexandre Mattei, Jes Frellsen
We revisit the theory of importance weighted variational inference (IWVI), a promising strategy for learning latent variable models. IWVI uses new variational bounds, known as Mont…