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pro vyhledávání: '"Mondragón, Esther"'
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. Our method is scalable and can generalise to multiple id
Externí odkaz:
http://arxiv.org/abs/2312.06400
Current conversational agents (CA) have seen improvement in conversational quality in recent years due to the influence of large language models (LLMs) like GPT3. However, two key categories of problem remain. Firstly there are the unique technical p
Externí odkaz:
http://arxiv.org/abs/2311.05450
In this paper, we propose a framework to extract the algebra of the transformations of worlds from the perspective of an agent. As a starting point, we use our framework to reproduce the symmetry-based representations from the symmetry-based disentan
Externí odkaz:
http://arxiv.org/abs/2310.01536
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. Unlike machine representat
Externí odkaz:
http://arxiv.org/abs/2205.09738
Akademický článek
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Publikováno v:
In Trends in Cognitive Sciences November 2017 21(11):822-825
Autor:
Mondragón, Esther, Hall, Geoffrey
Publikováno v:
In Learning and Motivation February 2015 49:14-22
Publikováno v:
In Computer Methods and Programs in Biomedicine May 2013 110(2):226-230
Publikováno v:
In Computer Methods and Programs in Biomedicine October 2012 108(1):346-355
Publikováno v:
Science, 2008 Mar . 319(5871), 1849-1851.
Externí odkaz:
https://www.jstor.org/stable/20053700