This page lists publications, preprints, and projects related to my work in machine learning. Comments and questions welcome.

**Symmetries, flat minima, and the conserved quantities of gradient flow**.

*With B. Zhao, R. Walters, R. Yu, and N. Dehmamy.*

International Conference on Learning Representations (ICLR), 2023.

**Quiver neural networks.**

*With R. Walters.*

Preprint, arXiv:2207.12773.

**Universal approximation and model compression for radial neural networks.**

*With T. van Laarhoven and R. Walters.*

Preprint, arXiv:2107.02550.

**Gradient flow and conserved quantities**.

These are notes stemming from and supplementing the paper *Symmetries, flat minima, and the conserved quantities of gradient flow* above (arXiv link). We explain several different characterizations of conserved quantities, provide examples, and explore conserved quantities in the context of a loss function that is invariant under a group action. Of particular interest are orthogonal group actions, which appear in the study of radial neural networks. We provide a description of the stablizer of a generic point in the parameter space of a radial neural network, which is related to the compression of network. We also consider a particular diagonalizable action.

**Kaggle**.

Minor learning projects for gaining familiarity with PyTorch. One of the projects is a computer vision analysis for distinguishing bees from wasps using a convolutional neural network (link to the Kaggle dataset). Another is a NLP sentiment analysis for IMDB movie reviews (link to the Kaggle dataset).

**Bias-Variance**.

An analysis of the bias-variance tradeoff in supervised statistical learning. We examine both the regression and classification settings.

**Decomposition of group-equivariant neural networks**.

Coming soon!