Zobrazeno 1 - 10
of 103
pro vyhledávání: '"WEI Junyi"'
Autor:
Meyer, Mark J., Wei, Junyi
Variational regression methods are an increasingly popular tool for their efficient estimation of complex. Given the mixed model representation of penalized effects, additive regression models with smoothed effects and scalar-on-function regression m
Externí odkaz:
http://arxiv.org/abs/2406.08168
Large language models (LLM) have emerged as a powerful tool for AI, with the key ability of in-context learning (ICL), where they can perform well on unseen tasks based on a brief series of task examples without necessitating any adjustments to the m
Externí odkaz:
http://arxiv.org/abs/2405.19592
Foundation models have emerged as a powerful tool for many AI problems. Despite the tremendous success of foundation models, effective adaptation to new tasks, particularly those with limited labels, remains an open question and lacks theoretical und
Externí odkaz:
http://arxiv.org/abs/2402.15017
Autor:
Zou, Xueyan, Li, Linjie, Wang, Jianfeng, Yang, Jianwei, Ding, Mingyu, Wei, Junyi, Yang, Zhengyuan, Li, Feng, Zhang, Hao, Liu, Shilong, Aravinthan, Arul, Lee, Yong Jae, Wang, Lijuan
Foundation models possess strong capabilities in reasoning and memorizing across modalities. To further unleash the power of foundation models, we present FIND, a generalized interface for aligning foundation models' embeddings with unified image and
Externí odkaz:
http://arxiv.org/abs/2312.07532
Neural networks have achieved remarkable empirical performance, while the current theoretical analysis is not adequate for understanding their success, e.g., the Neural Tangent Kernel approach fails to capture their key feature learning ability, whil
Externí odkaz:
http://arxiv.org/abs/2310.12408
An important characteristic of neural networks is their ability to learn representations of the input data with effective features for prediction, which is believed to be a key factor to their superior empirical performance. To better understand the
Externí odkaz:
http://arxiv.org/abs/2206.01717
Publikováno v:
In International Journal of Applied Earth Observation and Geoinformation March 2024 127
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Akademický článek
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Publikováno v:
In International Journal of Applied Earth Observation and Geoinformation November 2023 124