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cs.LG2024
A Bayesian Model Selection Criterion for Selecting Pretraining Checkpoints
Michael Munn, Susan Wei
Recent advances in artificial intelligence have been fueled by the development of foundation models such as BERT, GPT, T5, and Vision Transformers. These models are first pretraine…
stat.ML2024
A Margin-based Multiclass Generalization Bound via Geometric Complexity
Michael Munn, Benoit Dherin, Javier Gonzalvo
There has been considerable effort to better understand the generalization capabilities of deep neural networks both as a means to unlock a theoretical understanding of their succe…
cs.LG2024
The Impact of Geometric Complexity on Neural Collapse in Transfer Learning
Michael Munn, Benoit Dherin, Javier Gonzalvo
Many of the recent remarkable advances in computer vision and language models can be attributed to the success of transfer learning via the pre-training of large foundation models.…