Zobrazeno 1 - 10
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pro vyhledávání: '"ZHANG, Wei"'
Autor:
Zhang, Wei
Autor:
Zhang, Wei
Motivated by a recent switch of online ad exchanges from second-price auctions to firstprice auctions, this thesis studies computational problems related to how an advertiser can select bids to maximize her cumulative reward when participating in a s
Externí odkaz:
https://hdl.handle.net/1721.1/153829
Experience replay serves as a key component in the success of online reinforcement learning (RL). Prioritized experience replay (PER) reweights experiences by the temporal difference (TD) error empirically enhancing the performance. However, few work
Externí odkaz:
http://arxiv.org/abs/2407.03995
We propose a quantum Rabi square model where both the nearest-neighbor and the next-nearest-neighbor photon hopping are allowed among four quantum Rabi systems located at the vertices of a square. By tuning the next-nearest hopping strength, we reali
Externí odkaz:
http://arxiv.org/abs/2407.03612
Autor:
Chen, Yenan, Zhang, Chuye, Gu, Pengxi, Qiu, Jianuo, Yin, Jiayi, Qiu, Nuofan, Huang, Guojing, Huang, Bangchao, Zhang, Zishang, Deng, Hui, Zhang, Wei, Wan, Fang, Song, Chaoyang
While the animals' Fin-to-Limb evolution has been well-researched in biology, such morphological transformation remains under-adopted in the modern design of advanced robotic limbs. This paper investigates a novel class of overconstrained locomotion
Externí odkaz:
http://arxiv.org/abs/2407.01050
Autor:
Zhou, Jiehui, Wang, Xumeng, Kam-Kwai, Wong, Zhang, Wei, Liu, Xingyu, Zhang, Juntian, Zhu, Minfeng, Chen, Wei
In causal inference, estimating Heterogeneous Treatment Effects (HTEs) from observational data is critical for understanding how different subgroups respond to treatments, with broad applications such as precision medicine and targeted advertising. H
Externí odkaz:
http://arxiv.org/abs/2407.01893
Autor:
Chen, Lan, Li, Dong, Wang, Xiao, Shao, Pengpeng, Zhang, Wei, Wang, Yaowei, Tian, Yonghong, Tang, Jin
Existing event stream-based pattern recognition models usually represent the event stream as the point cloud, voxel, image, etc., and design various deep neural networks to learn their features. Although considerable results can be achieved in simple
Externí odkaz:
http://arxiv.org/abs/2406.18845
Retrieval-augmented generation has gained popularity as a framework to enhance large language models with external knowledge. However, its effectiveness hinges on the retrieval robustness of the model. If the model lacks retrieval robustness, its per
Externí odkaz:
http://arxiv.org/abs/2406.18134
The key challenge in semantic search is to create models that are both accurate and efficient in pinpointing relevant sentences for queries. While BERT-style bi-encoders excel in efficiency with pre-computed embeddings, they often miss subtle nuances
Externí odkaz:
http://arxiv.org/abs/2406.17262