no code implementations • 28 May 2024 • Johann Bauer, Sheldon West, Eduardo Alonso, Mark Broom
We present two variants of a multi-agent reinforcement learning algorithm based on evolutionary game theoretic considerations.
no code implementations • 11 Dec 2023 • Aaron Mir, Eduardo Alonso, Esther Mondragón
We propose a novel talking head synthesis pipeline called "DiT-Head", which is based on diffusion transformers and uses audio as a condition to drive the denoising process of a diffusion model.
no code implementations • 9 Nov 2023 • Alex Clay, Eduardo Alonso, Esther Mondragón
Current conversational agents (CA) have seen improvement in conversational quality in recent years due to the influence of large language models (LLMs) like GPT3.
1 code implementation • 2 Oct 2023 • Alexander Dean, Eduardo Alonso, Esther Mondragon
In this paper, we propose a framework to extract the algebra of the transformations of worlds from the perspective of an agent.
no code implementations • 11 Jan 2023 • Chi-Hang Suen, Eduardo Alonso
To replace data augmentation, this paper proposed a method called SLAP to intensify experience to speed up machine learning and reduce the sample size.
no code implementations • 6 Jun 2022 • Vince Jankovics, Michael Garcia Ortiz, Eduardo Alonso
Recent deep reinforcement learning (DRL) successes rely on end-to-end learning from fixed-size observational inputs (e. g. image, state-variables).
no code implementations • 19 May 2022 • Corina Catarau-Cotutiu, Esther Mondragon, Eduardo Alonso
Inspired by cognitive theories of creativity, this paper introduces a computational model (AIGenC) that lays down the necessary components to enable artificial agents to learn, use and generate transferable representations.
no code implementations • 26 Sep 2021 • Aram Ter-Sarkisov, Eduardo Alonso
In this paper we introduce Local Logo Generative Adversarial Network (LL-GAN) that uses regional features extracted from Faster R-CNN for logo generation.
no code implementations • 23 Feb 2020 • Fatemeh Najibi, Dimitra Apostolopoulou, Eduardo Alonso
First, we categorise the data into four groups based on solar output and time by using k-means clustering.
no code implementations • 23 Aug 2018 • Nathan J Olliverre, Guang Yang, Gregory Slabaugh, Constantino Carlos Reyes-Aldasoro, Eduardo Alonso
Magnetic Resonance Spectroscopy (MRS) provides valuable information to help with the identification and understanding of brain tumors, yet MRS is not a widely available medical imaging modality.
1 code implementation • 23 Apr 2018 • Artur d'Avila Garcez, Aimore Resende Riquetti Dutra, Eduardo Alonso
Deep Reinforcement Learning (deep RL) has made several breakthroughs in recent years in applications ranging from complex control tasks in unmanned vehicles to game playing.