Summary of Spectral Introspection Identifies Group Training Dynamics in Deep Neural Networks For Neuroimaging, by Bradley T. Baker et al.
Spectral Introspection Identifies Group Training Dynamics in Deep Neural Networks for Neuroimaging
by Bradley T. Baker, Vince D. Calhoun, Sergey M. Plis
First submitted to arxiv on: 17 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Image and Video Processing (eess.IV); Neurons and Cognition (q-bio.NC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This novel introspection framework for Deep Learning on Neuroimaging data enables researchers to better understand the emergence of model behaviors such as bias, overfitting, and overparametrization. By exploiting the natural structure of gradient computations via singular value decomposition during reverse-mode auto-differentiation, this method allows for the study of training dynamics on the fly. This is in contrast to post-hoc introspection techniques that require fully-trained models for evaluation. The framework also enables the decomposition of gradients based on which samples belong to particular groups of interest. For instance, the gradient spectra for several common deep learning models differ between schizophrenia and control participants from the COBRE study, revealing specific training dynamics helpful for further analysis. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how neural networks work by giving us a new way to look at how they learn. Right now, it’s hard for humans to figure out why certain things happen in complex systems like this. This is especially important when we’re trying to prevent or understand risks that come with using these systems. The researchers created a special tool that can help us see what’s happening inside the system while it’s still learning. They used this tool on some brain scan data and found out that different types of models behave differently in people with schizophrenia versus those without. This is helpful because it gives us clues about how to make better models. |
Keywords
* Artificial intelligence * Deep learning * Overfitting
