This is a casual explanation of our paper that introduced the concept of Diffusion Spectral Entropy (DSE) and Diffusion Spectral Mutual Information (DSMI).
Please find the blog page, compiled in format of a paper, here.
You are very welcome to cite the following papers (the “DSE2024” version preferred) instead if you find this blog helpful.
These are different versions of the same work. “DSE2024” is the final published version at an IEEE information theory conference. “DSE2024_arxiv” is the preprint version with the most amount of details. “DSE2023” is an ICML workshop version.
@inproceedings{DSE2024,
title={Assessing Neural Network Representations During Training Using NoiseResilient Diffusion Spectral Entropy},
author={Liao, Danqi and Liu, Chen and Christensen, Benjamin W and Tong, Alexander and Huguet, Guillaume and Wolf, Guy and Nickel, Maximilian and Adelstein, Ian and Krishnaswamy, Smita},
booktitle={2024 58th Annual Conference on Information Sciences and Systems (CISS)},
pages={1--6},
year={2024},
organization={IEEE}
}
@inproceedings{DSE2023,
title={Assessing Neural Network Representations During Training Using Data Diffusion Spectra},
author={Liao, Danqi and Liu, Chen and Christensen, Benjamin W and Tong, Alexander and Huguet, Guillaume and Wolf, Guy and Nickel, Maximilian and Adelstein, Ian and Krishnaswamy, Smita},
booktitle={International Conference on Machine Learning (ICML) Workshop on Topology, Algebra, and Geometry in Machine Learning},
year={2023},
}
@article{DSE2024_arxiv,
title={Assessing Neural Network Representations During Training Using NoiseResilient Diffusion Spectral Entropy},
author={Liao, Danqi and Liu, Chen and Christensen, Benjamin W and Tong, Alexander and Huguet, Guillaume and Wolf, Guy and Nickel, Maximilian and Adelstein, Ian and Krishnaswamy, Smita},
journal={arXiv preprint arXiv:2312.04823},
year={2023}
}