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Lie Algebra Canonicalization: Equivariant Neural Operators under arbitrary Lie Groups
The quest for robust and generalizable machine learning models has driven recent interest in exploiting symmetries through equivariant …
Z. Shumaylov, P. Zaika, J. Rowbottom, F. Sherry, M. Weber, C.-B. Schönlieb
PDF Project
Unitary convolutions for learning on graphs and groups
Group-convolutional architectures, which encode symmetries as inductive bias, have shown great success in applications, but can suffer …
B. T. Kiani, L. Fesser, M. Weber
PDF Project
Hardness of Learning Neural Networks under the Manifold Hypothesis
The manifold hypothesis presumes that high-dimensional data lies on or near a low-dimensional manifold. While the utility of encoding …
B. T. Kiani, J. Wang, M. Weber
PDF Project
Contrastive Poincaré Maps for single-cell data analysis
Complex hierarchies and branching patterns underlie numerous biological processes, from organismic development to signal divergence …
N. Bhasker, H. Chung, L. Boucherie, V. Kim, S. Speidel, M. Weber
PDF Project
Effective Structural Encodings via Local Curvature Profiles
Structural and Positional Encodings can significantly improve the performance of Graph Neural Networks in downstream tasks. Recent …
L. Fesser, M. Weber
PDF Project
On the Hardness of Learning under Symmetries
We study the problem of learning equivariant neural networks via gradient descent. A recent line of learning theoretic research has …
B. T. Kiani, T. Le, H. Lawrence, S. Jegelka, M. Weber
PDF Project
Mitigating Over-Smoothing and Over-Squashing using Augmentations of Forman-Ricci Curvature
While Graph Neural Networks (GNNs) have been successfully leveraged for learning on graph-structured data across domains, several …
L. Fesser, M. Weber
PDF Project
Sampling Informative Positives Pairs in Contrastive Learning
Contrastive Learning is a paradigm for learning representation functions that recover useful similarity structure in a dataset based on …
M. Weber, P. Bachman
PDF Project
Global optimality for Euclidean CCCP under Riemannian convexity
We study geodesically convex problems that can be written as a difference of Euclidean convex functions. This structure arises in key …
M. Weber, S. Sra
Preprint Project
LegalRelectra: Mixed-domain Language Modeling for Long-range Legal Text Comprehension
The application of Natural Language Processing (NLP) to specialized domains, such as the law, has recently received a surge of …
W. Hua, Y. Zhang, Z. Chen, J. Li, M. Weber
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