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pro vyhledávání: '"Xie, Johnathan"'
The effectiveness of large language models (LLMs) is not only measured by their ability to generate accurate outputs but also by their calibration-how well their confidence scores reflect the probability of their outputs being correct. While unsuperv
Externí odkaz:
http://arxiv.org/abs/2409.19817
Self-supervised learning excels in learning representations from large amounts of unlabeled data, demonstrating success across multiple data modalities. Yet, extending self-supervised learning to new modalities is non-trivial because the specifics of
Externí odkaz:
http://arxiv.org/abs/2402.14789
Autor:
Xie, Johnathan, Zheng, Shuai
Recent approaches have shown that training deep neural networks directly on large-scale image-text pair collections enables zero-shot transfer on various recognition tasks. One central issue is how this can be generalized to object detection, which i
Externí odkaz:
http://arxiv.org/abs/2109.12066
Autor:
Xie, Johnathan, Zheng, Shuai
Publikováno v:
2022 IEEE International Conference on Data Mining Workshops (ICDMW).
Recent approaches have shown that training deep neural networks directly on large-scale image-text pair collections enables zero-shot transfer on various recognition tasks. One central issue is how this can be generalized to object detection, which i