3 citations · 3 across the 3 of their papers we have counts for
3 papers
Posterior Sampling of Probabilistic Word Embeddings
Väinö Yrjänäinen, Isac Boström, Måns Magnusson +1
Quantifying uncertainty in word embeddings is crucial for reliable inference from textual data. However, existing Bayesian methods such as Hamiltonian Monte Carlo (HMC) and mean-fi…
Noise Sensitivity and Stability of Deep Neural Networks for Binary Classification
Johan Jonasson, Jeffrey E. Steif, Olof Zetterqvist
A first step is taken towards understanding often observed non-robustness phenomena of deep neural net (DNN) classifiers. This is done from the perspective of Boolean functions by…
Optimal subsampling designs
Henrik Imberg, Marina Axelson-Fisk, Johan Jonasson
Subsampling is commonly used to overcome computational and economical bottlenecks in the analysis of finite populations and massive datasets. Existing methods are often limited in…