Year: 2022
Type: preprint
Abstract: Normalizing flows are powerful non-parametric statistical models that function as a hybrid between density estimators and generative models. Current learning algorithms for normalizing flows assume that data points are sampled independently, an assumption that is frequently violated in practice, which may lead to erroneous density estimation and data generation. We propose a likelihood objective of normalizing flows incorporating dependencies between the data points, for which we derive a flexible and efficient learning algorithm suitable for different dependency structures. We show that respecting dependencies between observations can improve empirical results on both synthetic and real-world data, and leads to higher statistical power in a downstream application to genome-wide association studies.
Source: arXiv (Cornell University)
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funders: U.S. Department of Defense, Alzheimer's Association, Bristol-Myers Squibb, Eli Lilly and Company, Pfizer, Biogen, University of Southern California, F. Hoffmann-La Roche, Meso Scale Diagnostics, Alzheimer's Disease Neuroimaging Initiative, BioClinica, Novartis Pharmaceuticals Corporation, Northern California Institute for Research and Education, Deutsche Forschungsgemeinschaft, Bundesministerium für Bildung und Forschung, Eisai, Servier, H. Lundbeck A/S, IXICO, National Institutes of Health, Genentech, Canadian Institutes of Health Research
Awards: G1012222616, G1298807586, G1332273124, G1554142629, G1593117279, G1650819308, G1880935189, G2100670906, G2152643862, G2189010064, G2340207221, G2355444668, G2423387114, G2471646051, G2522063444, G2835601961, G29951154, G3131374986, G3216574877, G3336781480, G3553156159, G3710456697, G3752451908, G3870774570, G3908963977, G3966393351, G4119534169, G43180419, G4475667458, G4501223628, G4800177743, G4853260319, G486260054, G5207404921, G5209990037, G528601746, G5324093878, G5365730499, G5376319505, G5654639750, G5740349702, G5850682332, G613619302, G6173816828, G6210868510, G6371410416, G6412110892, G6447063303, G6719828067, G6792506723, G6899163935, G6948409098, G72415811, G7298697282, G764699806, G7799614282, G781725486, G8228018387, G8281378126, G839208290, G8730200936, G8852128998, G8895907669, G8897063270, G8957659042, G907317167
OpenAlex ID: W4298187228