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Found 8 papers, 3 papers with code
Hamiltonian Matching for Symplectic Neural Integrators
Hamilton's equations of motion form a fundamental framework in various branches of physics, including astronomy, quantum mechanics, particle physics, and climate science.
Lie Algebra Canonicalization: Equivariant Neural Operators under arbitrary Lie Groups
In the context of PDE solvers, recent works have shown that Lie point symmetries can be a useful inductive bias for Physics-Informed Neural Networks (PINNs) through data and loss augmentation.
Score-based pullback Riemannian geometry
Data-driven Riemannian geometry has emerged as a powerful tool for interpretable representation learning, offering improved efficiency in downstream tasks.
Weakly Convex Regularisers for Inverse Problems: Convergence of Critical Points and Primal-Dual Optimisation
Variational regularisation is the primary method for solving inverse problems, and recently there has been considerable work leveraging deeply learned regularisation for enhanced performance.
Provably Convergent Data-Driven Convex-Nonconvex Regularization
An emerging new paradigm for solving inverse problems is via the use of deep learning to learn a regularizer from data.
The Curse of Recursion: Training on Generated Data Makes Models Forget
It is now clear that large language models (LLMs) are here to stay, and will bring about drastic change in the whole ecosystem of online text and images.
Manipulating SGD with Data Ordering Attacks
Machine learning is vulnerable to a wide variety of attacks.
Learned convex regularizers for inverse problems
We consider the variational reconstruction framework for inverse problems and propose to learn a data-adaptive input-convex neural network (ICNN) as the regularization functional.
