Zobrazeno 1 - 10
of 31
pro vyhledávání: '"Aubret, Arthur"'
Due to significant variations in the projection of the same object from different viewpoints, machine learning algorithms struggle to recognize the same object across various perspectives. In contrast, toddlers quickly learn to recognize objects from
Externí odkaz:
http://arxiv.org/abs/2411.01969
Self-supervised learning (SSL) has revolutionized visual representation learning, but has not achieved the robustness of human vision. A reason for this could be that SSL does not leverage all the data available to humans during learning. When learni
Externí odkaz:
http://arxiv.org/abs/2407.06704
Humans judge the similarity of two objects not just based on their visual appearance but also based on their semantic relatedness. However, it remains unclear how humans learn about semantic relationships between objects and categories. One important
Externí odkaz:
http://arxiv.org/abs/2405.05143
Color constancy (CC) describes the ability of the visual system to perceive an object as having a relatively constant color despite changes in lighting conditions. While CC and its limitations have been carefully characterized in humans, it is still
Externí odkaz:
http://arxiv.org/abs/2404.08127
Publikováno v:
"Caregiver Talk Shapes Toddler Vision: A Computational Study of Dyadic Play," 2023 IEEE International Conference on Development and Learning (ICDL), Macau, China, 2023, pp. 67-72
Infants' ability to recognize and categorize objects develops gradually. The second year of life is marked by both the emergence of more semantic visual representations and a better understanding of word meaning. This suggests that language input may
Externí odkaz:
http://arxiv.org/abs/2312.04118
Autor:
Mattern, Dominik, Schumacher, Pierre, López, Francisco M., Raabe, Marcel C., Ernst, Markus R., Aubret, Arthur, Triesch, Jochen
Human intelligence and human consciousness emerge gradually during the process of cognitive development. Understanding this development is an essential aspect of understanding the human mind and may facilitate the construction of artificial minds wit
Externí odkaz:
http://arxiv.org/abs/2312.04318
The reinforcement learning (RL) research area is very active, with an important number of new contributions; especially considering the emergent field of deep RL (DRL). However a number of scientific and technical challenges still need to be resolved
Externí odkaz:
http://arxiv.org/abs/2209.08890
Biological vision systems are unparalleled in their ability to learn visual representations without supervision. In machine learning, self-supervised learning (SSL) has led to major advances in forming object representations in an unsupervised fashio
Externí odkaz:
http://arxiv.org/abs/2207.13492
Recent time-contrastive learning approaches manage to learn invariant object representations without supervision. This is achieved by mapping successive views of an object onto close-by internal representations. When considering this learning approac
Externí odkaz:
http://arxiv.org/abs/2205.06198
The optimal way for a deep reinforcement learning (DRL) agent to explore is to learn a set of skills that achieves a uniform distribution of states. Following this,we introduce DisTop, a new model that simultaneously learns diverse skills and focuses
Externí odkaz:
http://arxiv.org/abs/2106.03853