4 citations · 16 across the 20 of their papers we have counts for
Showing stat.MLShow all
2 papers · 1 filter
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.ML2024★ 1 cited
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…