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Found 4 papers, 4 papers with code
Optimizing the Noise in Self-Supervised Learning: from Importance Sampling to Noise-Contrastive Estimation
Nevertheless, we soberly conclude that the optimal noise may be hard to sample from, and the gain in efficiency can be modest compared to choosing the noise distribution equal to the data's.
The Optimal Noise in Noise-Contrastive Learning Is Not What You Think
Learning a parametric model of a data distribution is a well-known statistical problem that has seen renewed interest as it is brought to scale in deep learning.
Deep Recurrent Encoder: A scalable end-to-end network to model brain signals
Understanding how the brain responds to sensory inputs is challenging: brain recordings are partial, noisy, and high dimensional; they vary across sessions and subjects and they capture highly nonlinear dynamics.
Uncovering the structure of clinical EEG signals with self-supervised learning
Our results suggest that SSL may pave the way to a wider use of deep learning models on EEG data.
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