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pro vyhledávání: '"Chen, Siyu"'
RGB-D has gradually become a crucial data source for understanding complex scenes in assisted driving. However, existing studies have paid insufficient attention to the intrinsic spatial properties of depth maps. This oversight significantly impacts
Externí odkaz:
http://arxiv.org/abs/2409.07995
In-context learning (ICL) is a cornerstone of large language model (LLM) functionality, yet its theoretical foundations remain elusive due to the complexity of transformer architectures. In particular, most existing work only theoretically explains h
Externí odkaz:
http://arxiv.org/abs/2409.10559
Chain-of-Thought (CoT) prompting and its variants have gained popularity as effective methods for solving multi-step reasoning problems using pretrained large language models (LLMs). In this work, we analyze CoT prompting from a statistical estimatio
Externí odkaz:
http://arxiv.org/abs/2408.14511
Autor:
Chen, Bobby, Chen, Siyu, Dowlatabadi, Jason, Hong, Yu Xuan, Iyer, Vinayak, Mantripragada, Uday, Narang, Rishabh, Pandey, Apoorv, Qin, Zijun, Sheikh, Abrar, Sun, Hongtao, Sun, Jiaqi, Walker, Matthew, Wei, Kaichen, Xu, Chen, Yang, Jingnan, Zhang, Allen T., Zhang, Guoqing
Budget allocation of marketplace levers, such as incentives for drivers and promotions for riders, has long been a technical and business challenge at Uber; understanding lever budget changes' impact and estimating cost efficiency to achieve predefin
Externí odkaz:
http://arxiv.org/abs/2407.19078
Autor:
Chen, Siyu, Guo, Qing
Employing a comprehensive survey of micro and small enterprises (MSEs) and the Digital Financial Inclusion Index in China, this study investigates the influence of fintech on MSE innovation empirically. Our findings indicate that fintech advancement
Externí odkaz:
http://arxiv.org/abs/2407.17293
We measure the performance of in-context learning as a function of task novelty and difficulty for open and closed questions. For that purpose, we created a novel benchmark consisting of hard scientific questions, each paired with a context of variou
Externí odkaz:
http://arxiv.org/abs/2407.02028
The agency problem emerges in today's large scale machine learning tasks, where the learners are unable to direct content creation or enforce data collection. In this work, we propose a theoretical framework for aligning economic interests of differe
Externí odkaz:
http://arxiv.org/abs/2407.01458
In this work, from a theoretical lens, we aim to understand why large language model (LLM) empowered agents are able to solve decision-making problems in the physical world. To this end, consider a hierarchical reinforcement learning (RL) model where
Externí odkaz:
http://arxiv.org/abs/2405.19883
The proliferation of internet technology has catalyzed the rapid development of digital finance, significantly impacting the optimization of resource allocation in China and exerting a substantial and enduring influence on the structure of employment
Externí odkaz:
http://arxiv.org/abs/2405.15486
By introducing the Fermat number transform into chromatic dispersion compensation and adaptive equalization, the computational complexity has been reduced by 68% compared with the con?ventional implementation. Experimental results validate its transm
Externí odkaz:
http://arxiv.org/abs/2405.04253