72 citations · 185 across the 18 of their papers we have counts for
3 papers · 1 filter
A Unified Framework for Tabular Generative Modeling: Loss Functions, Benchmarks, and Improved Multi-objective Bayesian Optimization Approaches
Minh H. Vu, Daniel Edler, Carl Wibom +3
Deep learning (DL) models require extensive data to achieve strong performance and generalization. Deep generative models (DGMs) offer a solution by synthesizing data. Yet current…
A Training Rate and Survival Heuristic for Inference and Robustness Evaluation (TRASHFIRE)
Charles Meyers, Mohammad Reza Saleh Sedghpour, Tommy Löfstedt +1
Machine learning models -- deep neural networks in particular -- have performed remarkably well on benchmark datasets across a wide variety of domains. However, the ease of finding…
Reproducibility of the Methods in Medical Imaging with Deep Learning
Attila Simko, Anders Garpebring, Joakim Jonsson +2
Concerns about the reproducibility of deep learning research are more prominent than ever, with no clear solution in sight. The relevance of machine learning research can only be i…