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stat.ML2026
Self-Supervised Learning from Noisy and Incomplete Data
Julián Tachella, Mike Davies
Many important problems in science and engineering involve inferring a signal from noisy and/or incomplete observations, where the observation process is known. Historically, this…
stat.ML2025
UNSURE: self-supervised learning with Unknown Noise level and Stein's Unbiased Risk Estimate
Julián Tachella, Mike Davies, Laurent Jacques
Recently, many self-supervised learning methods for image reconstruction have been proposed that can learn from noisy data alone, bypassing the need for ground-truth references. Mo…