Zobrazeno 1 - 10
of 55
pro vyhledávání: '"Duque, Miguel"'
Data-driven approaches to philosophy have emerged as a valuable tool for studying the history of the discipline. However, most studies in this area have focused on a limited number of journals from specific regions and subfields. We expand the scope
Externí odkaz:
http://arxiv.org/abs/2412.04236
Autor:
González-Duque, Miguel, Michael, Richard, Bartels, Simon, Zainchkovskyy, Yevgen, Hauberg, Søren, Boomsma, Wouter
Optimizing discrete black-box functions is key in several domains, e.g. protein engineering and drug design. Due to the lack of gradient information and the need for sample efficiency, Bayesian optimization is an ideal candidate for these tasks. Seve
Externí odkaz:
http://arxiv.org/abs/2406.04739
Autor:
Michael, Richard, Bartels, Simon, González-Duque, Miguel, Zainchkovskyy, Yevgen, Frellsen, Jes, Hauberg, Søren, Boomsma, Wouter
To optimize efficiently over discrete data and with only few available target observations is a challenge in Bayesian optimization. We propose a continuous relaxation of the objective function and show that inference and optimization can be computati
Externí odkaz:
http://arxiv.org/abs/2404.17452
Autor:
Sudhakaran, Shyam, González-Duque, Miguel, Glanois, Claire, Freiberger, Matthias, Najarro, Elias, Risi, Sebastian
Procedural Content Generation (PCG) is a technique to generate complex and diverse environments in an automated way. However, while generating content with PCG methods is often straightforward, generating meaningful content that reflects specific int
Externí odkaz:
http://arxiv.org/abs/2302.05981
Autor:
Jaquier, Noémie, Rozo, Leonel, González-Duque, Miguel, Borovitskiy, Viacheslav, Asfour, Tamim
Human motion taxonomies serve as high-level hierarchical abstractions that classify how humans move and interact with their environment. They have proven useful to analyse grasps, manipulation skills, and whole-body support poses. Despite substantial
Externí odkaz:
http://arxiv.org/abs/2210.01672
Deep generative models can automatically create content of diverse types. However, there are no guarantees that such content will satisfy the criteria necessary to present it to end-users and be functional, e.g. the generated levels could be unsolvab
Externí odkaz:
http://arxiv.org/abs/2206.00106
In nature, the process of cellular growth and differentiation has lead to an amazing diversity of organisms -- algae, starfish, giant sequoia, tardigrades, and orcas are all created by the same generative process. Inspired by the incredible diversity
Externí odkaz:
http://arxiv.org/abs/2201.12360
Autor:
Arvanitidis, Georgios, González-Duque, Miguel, Pouplin, Alison, Kalatzis, Dimitris, Hauberg, Søren
Latent space geometry has shown itself to provide a rich and rigorous framework for interacting with the latent variables of deep generative models. The existing theory, however, relies on the decoder being a Gaussian distribution as its simple repar
Externí odkaz:
http://arxiv.org/abs/2106.05367
In games, as well as many user-facing systems, adapting content to users' preferences and experience is an important challenge. This paper explores a novel method to realize this goal in the context of dynamic difficulty adjustment (DDA). Here the ai
Externí odkaz:
http://arxiv.org/abs/2105.08484
Methods for dynamic difficulty adjustment allow games to be tailored to particular players to maximize their engagement. However, current methods often only modify a limited set of game features such as the difficulty of the opponents, or the availab
Externí odkaz:
http://arxiv.org/abs/2005.07677