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Found 8 papers, 1 papers with code
Weak Correlations as the Underlying Principle for Linearization of Gradient-Based Learning Systems
As a case in point, we showcase this weak correlations structure within neural networks in the large width limit.
Turbulence Scaling from Deep Learning Diffusion Generative Models
Complex spatial and temporal structures are inherent characteristics of turbulent fluid flows and comprehending them poses a major challenge.
The Universal Statistical Structure and Scaling Laws of Chaos and Turbulence
We show that from the RMT perspective, the turbulence Gram matrices lie in the same universality class as quantum chaotic rather than integrable systems, and the data exhibits power-law scalings in the bulk of its eigenvalues which are vastly different from uncorrelated classical chaos, random data, natural images.
The Underlying Scaling Laws and Universal Statistical Structure of Complex Datasets
We study universal traits which emerge both in real-world complex datasets, as well as in artificially generated ones.
Neural Network Complexity of Chaos and Turbulence
Chaos and turbulence are complex physical phenomena, yet a precise definition of the complexity measure that quantifies them is still lacking.
Semi-supervised Learning of Partial Differential Operators and Dynamical Flows
As a result, it successfully propagates initial conditions in continuous time steps by employing the general composition properties of the partial differential operators.
Unsupervised Disentanglement with Tensor Product Representations on the Torus
This way, the obtained representations are naturally disentangled.
Entanglement Diagnostics for Efficient Quantum Computation
We consider information spreading measures in randomly initialized variational quantum circuits and introduce entanglement diagnostics for efficient variational quantum/classical computations.
