2 citations · 2 across the 4 of their papers we have counts for
4 papers
Annotations Mitigate Post-Training Mode Collapse
Jacob Mitchell Springer, Madhu Advani, Lukas Aichberger +7
Post-training (via supervised fine-tuning) improves instruction-following, but often induces semantic mode collapse by biasing models toward low-entropy fine-tuning data at the exp…
Self-Supervised Learning with Gaussian Processes
Yunshan Duan, Sinead Williamson
Self supervised learning (SSL) is a machine learning paradigm where models learn to understand the underlying structure of data without explicit supervision from labeled samples. T…
Posterior Uncertainty Quantification in Neural Networks using Data Augmentation
Luhuan Wu, Sinead Williamson
In this paper, we approach the problem of uncertainty quantification in deep learning through a predictive framework, which captures uncertainty in model parameters by specifying o…
Bootstrap Your Own Variance
Polina Turishcheva, Jason Ramapuram, Sinead Williamson +3
Understanding model uncertainty is important for many applications. We propose Bootstrap Your Own Variance (BYOV), combining Bootstrap Your Own Latent (BYOL), a negative-free Self-…