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pro vyhledávání: '"RAHIMI, Amir"'
Autor:
Rahimi, Amir, D'Amario, Vanessa, Yamada, Moyuru, Takemoto, Kentaro, Sasaki, Tomotake, Boix, Xavier
Systematic generalization is a crucial aspect of intelligence, which refers to the ability to generalize to novel tasks by combining known subtasks and concepts. One critical factor that has been shown to influence systematic generalization is the di
Externí odkaz:
http://arxiv.org/abs/2309.08798
Autor:
Farshidi, Siamak, Rezaee, Kiyan, Mazaheri, Sara, Rahimi, Amir Hossein, Dadashzadeh, Ali, Ziabakhsh, Morteza, Eskandari, Sadegh, Jansen, Slinger
Context: User intent modeling is a crucial process in Natural Language Processing that aims to identify the underlying purpose behind a user's request, enabling personalized responses. With a vast array of approaches introduced in the literature (ove
Externí odkaz:
http://arxiv.org/abs/2308.08496
Publikováno v:
In Chemical Engineering Research and Design December 2024 212:121-133
Autor:
Rahimi, Amir Reza, Sevilla-Pavón, Ana
Publikováno v:
In Computers and Education: Artificial Intelligence December 2024 7
To obtain 3D annotations, we are restricted to controlled environments or synthetic datasets, leading us to 3D datasets with less generalizability to real-world scenarios. To tackle this issue in the context of semi-supervised 3D hand shape and pose
Externí odkaz:
http://arxiv.org/abs/2111.15199
This paper proposes the CogSense system, which is inspired by sense-making cognition and perception in the mammalian brain to perform perception error detection and perception parameter adaptation using probabilistic signal temporal logic. As a speci
Externí odkaz:
http://arxiv.org/abs/2107.10456
Detecting novel objects from few examples has become an emerging topic in computer vision recently. However, these methods need fully annotated training images to learn new object categories which limits their applicability in real world scenarios su
Externí odkaz:
http://arxiv.org/abs/2103.14162
Publikováno v:
In Ocean Engineering 15 January 2024 292
Autor:
Rahimi, Amir, Mensink, Thomas, Gupta, Kartik, Ajanthan, Thalaiyasingam, Sminchisescu, Cristian, Hartley, Richard
Calibration of neural networks is a critical aspect to consider when incorporating machine learning models in real-world decision-making systems where the confidence of decisions are equally important as the decisions themselves. In recent years, the
Externí odkaz:
http://arxiv.org/abs/2006.12807