A new graph-based approach addresses the shortcomings in Dimensionality Reduction (DR) methods, revealing hidden neighborhood structures in high-dimensional data.
Neural Hamiltonian ODEs push the limits of learning from dynamic systems, offering a solution to the challenge of partially observed data through physics-informed constraints.
A new study sheds light on the elusive causes of instability in deep neural networks. By leveraging non-Hermitian operator theory, researchers reveal insights into training challenges.
