19 citations · 54 across the 11 of their papers we have counts for
13 papers
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…
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…
Bounds all around: training energy-based models with bidirectional bounds
Cong Geng, Jia Wang, Zhiyong Gao +2
Energy-based models (EBMs) provide an elegant framework for density estimation, but they are notoriously difficult to train. Recent work has established links to generative adversa…
Kernel-Matrix Determinant Estimates from stopped Cholesky Decomposition
Simon Bartels, Wouter Boomsma, Jes Frellsen +1
Algorithms involving Gaussian processes or determinantal point processes typically require computing the determinant of a kernel matrix. Frequently, the latter is computed from the…