no code implementations • 10 Nov 2023 • Tim Whittaker, Romuald A. Janik, Yaron Oz
Complex spatial and temporal structures are inherent characteristics of turbulent fluid flows and comprehending them poses a major challenge.
1 code implementation • 7 Nov 2023 • Romuald A. Janik
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.
no code implementations • 24 Nov 2022 • Tim Whittaker, Romuald A. Janik, Yaron Oz
Chaos and turbulence are complex physical phenomena, yet a precise definition of the complexity measure that quantifies them is still lacking.
no code implementations • 16 Sep 2021 • Romuald A. Janik
We analyze the spaces of images encoded by generative neural networks of the BigGAN architecture.
no code implementations • 22 Jun 2020 • Romuald A. Janik, Aleksandra Nowak
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.
no code implementations • 8 Jun 2020 • Romuald A. Janik, Przemek Witaszczyk
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.
1 code implementation • 19 Feb 2020 • Romuald A. Janik, Aleksandra Nowak
We perform a massive evaluation of neural networks with architectures corresponding to random graphs of various types.
1 code implementation • 24 Sep 2019 • Romuald A. Janik
We translate the problem of calculating the entropy of a set of binary configurations/signals into a sequence of supervised classification tasks.