Abstract: Graph neural networks (GNNs) are effective at learning representations from network data but face challenges with large graphs, which lack the Euclidean structure of time-series and image data. However, graphs have limits, such as the graphon—a bounded symmetric kernel that serves as both a random graph model and a …
Abstract: In this talk I will describe recent progress in characterizing structure that emerges during the training of deep neural networks for classification. Neural Collapse is a phenomenon that emerges during the training of deep classifiers in which the top-layer feature embeddings of samples from the same class tend to …
Abstract: Although incorporating causal concepts into deep learning shows promise for increasing explainability, fairness, and robustness, existing methods require unrealistic assumptions or aim to recover the full latent causal model. This talk proposes an alternative: domain counterfactuals. Domain counterfactuals ask a more concrete question: “What would a sample look like …