File size: 2,819 Bytes
5e0b3dd | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 | # This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!
cff-version: 1.2.0
title: >-
Improving Single Noise Level Diffusion Samplers with
Restricted Gaussian Oracles
message: 'If you use this code for your projects, please cite:'
type: software
authors:
- given-names: Leello
family-names: Dadi
email: leello.dadi@epfl.ch
affiliation: 'EPFL STI IEM LIONS, Lausanne, Switzerland'
orcid: 'https://orcid.org/0000-0003-2580-4913'
- given-names: Andrej
family-names: Janchevski
email: andrej.janchevski@epfl.ch
affiliation: 'EPFL STI IEM LIONS, Lausanne, Switzerland'
orcid: 'https://orcid.org/0000-0001-9568-0966'
- given-names: Volkan
family-names: Cevher
email: volkan.cevher@epfl.ch
affiliation: 'EPFL STI IEM LIONS, Lausanne, Switzerland'
orcid: 'https://orcid.org/0000-0002-5004-201X'
identifiers:
- type: url
value: 'https://openreview.net/forum?id=xkiI5tou6J'
repository-code: >-
https://github.com/Bani57/multi-prox-diffusion-iclr-delta-2025
abstract: >-
Diffusion models and diffusion Monte-Carlo schemes that
sample from unnormalized log-densities, both rely on
denoisers ( or score estimates) at different noise scales.
This complicates the sampling process as denoising
schedules require careful tuning and nested inner-MCMC
loops. In this work, we propose a single noise level
sampling procedure that only requires a single low-noise
denoiser. Our framework results from improvements we bring
to the multimeasurement Walk-Jump sampler of Saremi et al.
2021 by mixing in ideas from the proximal sampler of Shen
et al. 2020. Our analysis shows that annealing (or
multiple noise scales) is unnecessary if one is willing to
pay an increased memory cost. We demonstrate this by
proposing an entirely log-concave sampling framework.
license: CC-BY-1.0
preferred-citation:
type: conference-paper
title: 'Improving Single Noise Level Denoising Samplers with Restricted Gaussian Oracles'
collection-title: 'ICLR 2025 Workshop on Deep Generative Model in Machine Learning: Theory, Principle and Efficacy'
year: 2025
url: 'https://openreview.net/forum?id=xkiI5tou6J'
authors:
- given-names: Leello
family-names: Dadi
email: leello.dadi@epfl.ch
affiliation: 'EPFL STI IEM LIONS, Lausanne, Switzerland'
orcid: 'https://orcid.org/0000-0003-2580-4913'
- given-names: Andrej
family-names: Janchevski
email: andrej.janchevski@epfl.ch
affiliation: 'EPFL STI IEM LIONS, Lausanne, Switzerland'
orcid: 'https://orcid.org/0000-0001-9568-0966'
- given-names: Volkan
family-names: Cevher
email: volkan.cevher@epfl.ch
affiliation: 'EPFL STI IEM LIONS, Lausanne, Switzerland'
orcid: 'https://orcid.org/0000-0002-5004-201X' |