Zobrazeno 1 - 10
of 35
pro vyhledávání: '"Vecchietti, Luiz Felipe"'
Autor:
Vecchietti, Luiz Felipe, Lee, Minji, Hangeldiyev, Begench, Jung, Hyunkyu, Park, Hahnbeom, Kim, Tae-Kyun, Cha, Meeyoung, Kim, Ho Min
Recent advancements in machine learning (ML) are transforming the field of structural biology. For example, AlphaFold, a groundbreaking neural network for protein structure prediction, has been widely adopted by researchers. The availability of easy-
Externí odkaz:
http://arxiv.org/abs/2409.17726
Proteins are complex molecules responsible for different functions in nature. Enhancing the functionality of proteins and cellular fitness can significantly impact various industries. However, protein optimization using computational methods remains
Externí odkaz:
http://arxiv.org/abs/2405.18986
Training agents via off-policy deep reinforcement learning (RL) requires a large memory, named replay memory, that stores past experiences used for learning. These experiences are sampled, uniformly or non-uniformly, to create the batches used for tr
Externí odkaz:
http://arxiv.org/abs/2212.13175
Identifying the camera pose for a given image is a challenging problem with applications in robotics, autonomous vehicles, and augmented/virtual reality. Lately, learning-based methods have shown to be effective for absolute camera pose estimation. H
Externí odkaz:
http://arxiv.org/abs/2211.10963
Relative camera pose estimation, i.e. estimating the translation and rotation vectors using a pair of images taken in different locations, is an important part of systems in augmented reality and robotics. In this paper, we present an end-to-end rela
Externí odkaz:
http://arxiv.org/abs/2202.12838
In urban cities, visual information on and along roadways is likely to distract drivers and lead to missing traffic signs and other accident-prone (AP) features. To avoid accidents due to missing these visual cues, this paper proposes a visual notifi
Externí odkaz:
http://arxiv.org/abs/2202.12788
In multi-agent reinforcement learning, the cooperative learning behavior of agents is very important. In the field of heterogeneous multi-agent reinforcement learning, cooperative behavior among different types of agents in a group is pursued. Learni
Externí odkaz:
http://arxiv.org/abs/2104.05931
This paper presents the power management of the nanogrid clusters assisted by a novel peer-to-peer(P2P) electricity trading. In our work, unbalance of power consumption among clusters is mitigated by the proposed P2P trading method. For power managem
Externí odkaz:
http://arxiv.org/abs/2009.00863
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