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pro vyhledávání: '"Yerramilli, Sahiti"'
Multimodal machine learning has gained significant attention in recent years due to its potential for integrating information from multiple modalities to enhance learning and decision-making processes. However, it is commonly observed that unimodal m
Externí odkaz:
http://arxiv.org/abs/2404.02359
Autor:
Yerramilli, Sahiti, Tamarapalli, Jayant Sravan, Kulkarni, Tanmay Girish, Francis, Jonathan, Nyberg, Eric
Deep Learning models are incredibly data-hungry and require very large labeled datasets for supervised learning. As a consequence, these models often suffer from overfitting, limiting their ability to generalize to real-world examples. Recent advance
Externí odkaz:
http://arxiv.org/abs/2404.02353
Understanding multimodal perception for embodied AI is an open question because such inputs may contain highly complementary as well as redundant information for the task. A relevant direction for multimodal policies is understanding the global trend
Externí odkaz:
http://arxiv.org/abs/2307.13850