Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 8 of 8 for “"Equivariant Neural Networks"”.
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Theoretical Properties of Equivariant Neural Networks
Modern deep neural networks often operate in highly overparameterized regimes, where they can interpolate training data while still generalizing effectively. This empirical phenomenon motivates the study of model expressivity and, in particular, of how architectural design choices shape …
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Prospects for Quantum Equivariant Neural Networks
Convolutional neural networks (CNNs) exploit translational invariance within images. Group equivariant neural networks comprise a natural generalization of convolutional neural networks by exploiting other symmetries arising through different group actions. Informally, a linear map is equivariant …
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Equivariant symmetry breaking sets
Equivariant neural networks (ENNs) have been shown to be extremely useful in many applications involving some underlying symmetries. However, equivariant networks are unable to produce lower symmetry outputs given a high symmetry input. Spontaneous symmetry breaking occurs in many physical systems …
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Geometric Deep Learning for Biomolecules
… biomolecular systems. We present methods using equivariant neural networks for geometrical protein representation learning, molecular representation learning for electron density prediction, and scalable molecular dynamics simulations using stochastic interpolants.
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Generative Machine Learning Models for RNA Structure Prediction and Design
… study on RNA structure prediction using equivariant neural networks within diffusion probabilistic models (DDPMs). Our folding model, named Klotho, captures local atomic interactions and structural features using SO(3)-equivariant message passing layers with a point cloud data …
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Simplifying Equivariant GPU Kernels through Tile-based Programming
E(3)-equivariant neural networks have demonstrated success across a wide range of 3D modeling tasks. Until recently, they were bottlenecked due to their high memory and wall-time requirements. In this thesis we first provide an overview of recent GPU kernel efforts by both academia and industry …
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Computational Tradeoffs and Symmetry in Polynomial Nonnegativity
… invariant theory, we construct an explicit equivariant map for nonnegativity certification. We further introduce an alternative approach using equivariant neural networks, analyzing their benefits and limitations.
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Equivariant Autoregressive Models for Molecular Generation
… basic chemical bonding rules. Recently, E(3)-equivariant neural networks that utilize higher-order rotationally-equivariant features have shown improved performance on a wide range of atomistic tasks, including structure generation. Previously, we have developed Symphony, an E(3)-equivariant …