Zobrazeno 1 - 10
of 32
pro vyhledávání: '"Mordvintsev, Alexander"'
Autor:
Arcas, Blaise Agüera y, Alakuijala, Jyrki, Evans, James, Laurie, Ben, Mordvintsev, Alexander, Niklasson, Eyvind, Randazzo, Ettore, Versari, Luca
The fields of Origin of Life and Artificial Life both question what life is and how it emerges from a distinct set of "pre-life" dynamics. One common feature of most substrates where life emerges is a marked shift in dynamics when self-replication ap
Externí odkaz:
http://arxiv.org/abs/2406.19108
Autor:
Pajouheshgar, Ehsan, Xu, Yitao, Mordvintsev, Alexander, Niklasson, Eyvind, Zhang, Tong, Süsstrunk, Sabine
Texture modeling and synthesis are essential for enhancing the realism of virtual environments. Methods that directly synthesize textures in 3D offer distinct advantages to the UV-mapping-based methods as they can create seamless textures and align m
Externí odkaz:
http://arxiv.org/abs/2311.02820
We introduce Biomaker CA: a Biome Maker project using Cellular Automata (CA). In Biomaker CA, morphogenesis is a first class citizen and small seeds need to grow into plant-like organisms to survive in a nutrient starved environment and eventually re
Externí odkaz:
http://arxiv.org/abs/2307.09320
Neural Cellular Automata (NCA) models have shown remarkable capacity for pattern formation and complex global behaviors stemming from local coordination. However, in the original implementation of NCA, cells are incapable of adjusting their own orien
Externí odkaz:
http://arxiv.org/abs/2302.10197
We present a differentiable formulation of abstract chemical reaction networks (CRNs) that can be trained to solve a variety of computational tasks. Chemical reaction networks are one of the most fundamental computational substrates used by nature. W
Externí odkaz:
http://arxiv.org/abs/2302.02714
Autor:
von Oswald, Johannes, Niklasson, Eyvind, Randazzo, Ettore, Sacramento, João, Mordvintsev, Alexander, Zhmoginov, Andrey, Vladymyrov, Max
At present, the mechanisms of in-context learning in Transformers are not well understood and remain mostly an intuition. In this paper, we suggest that training Transformers on auto-regressive objectives is closely related to gradient-based meta-lea
Externí odkaz:
http://arxiv.org/abs/2212.07677
Modeling the ability of multicellular organisms to build and maintain their bodies through local interactions between individual cells (morphogenesis) is a long-standing challenge of developmental biology. Recently, the Neural Cellular Automata (NCA)
Externí odkaz:
http://arxiv.org/abs/2205.01681
We study the problem of example-based procedural texture synthesis using highly compact models. Given a sample image, we use differentiable programming to train a generative process, parameterised by a recurrent Neural Cellular Automata (NCA) rule. C
Externí odkaz:
http://arxiv.org/abs/2111.13545
Reaction-Diffusion (RD) systems provide a computational framework that governs many pattern formation processes in nature. Current RD system design practices boil down to trial-and-error parameter search. We propose a differentiable optimization meth
Externí odkaz:
http://arxiv.org/abs/2107.06862
Neural Cellular Automata (NCA) have shown a remarkable ability to learn the required rules to "grow" images, classify morphologies, segment images, as well as to do general computation such as path-finding. We believe the inductive prior they introdu
Externí odkaz:
http://arxiv.org/abs/2105.07299