Search Results for author:
Found 8 papers, 3 papers with code
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.
Aspects of human memory and Large Language Models
Large Language Models (LLMs) are huge artificial neural networks which primarily serve to generate text, but also provide a very sophisticated probabilistic model of language use.
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.
Aesthetics and neural network image representations
We analyze the spaces of images encoded by generative neural networks of the BigGAN architecture.
Neural networks adapting to datasets: learning network size and topology
We introduce a flexible setup allowing for a neural network to learn both its size and topology during the course of a standard gradient-based training.
Complexity for deep neural networks and other characteristics of deep feature representations
We define a notion of complexity, which quantifies the nonlinearity of the computation of a neural network, as well as a complementary measure of the effective dimension of feature representations.
Analyzing Neural Networks Based on Random Graphs
We perform a massive evaluation of neural networks with architectures corresponding to random graphs of various types.
Entropy from Machine Learning
We translate the problem of calculating the entropy of a set of binary configurations/signals into a sequence of supervised classification tasks.
