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pro vyhledávání: '"Meng, Fanxu"'
Autor:
Meng, Fanxu, Zhou, Xiangzhen
Quantum computing presents a compelling prospect for revolutionizing the field of combinatorial optimization, in virtue of the unique attributes of quantum mechanics such as superposition and entanglement. The Quantum Approximate Optimization Algorit
Externí odkaz:
http://arxiv.org/abs/2407.12242
Autor:
Shi, Yu-Zhe, Hou, Haofei, Bi, Zhangqian, Meng, Fanxu, Wei, Xiang, Ruan, Lecheng, Wang, Qining
Accurate representation of procedures in restricted scenarios, such as non-standardized scientific experiments, requires precise depiction of constraints. Unfortunately, Domain-specific Language (DSL), as an effective tool to express constraints stru
Externí odkaz:
http://arxiv.org/abs/2406.12324
To parameter-efficiently fine-tune (PEFT) large language models (LLMs), the low-rank adaptation (LoRA) method approximates the model changes $\Delta W \in \mathbb{R}^{m \times n}$ through the product of two matrices $A \in \mathbb{R}^{m \times r}$ an
Externí odkaz:
http://arxiv.org/abs/2404.02948
The human brain is naturally equipped to comprehend and interpret visual information rapidly. When confronted with complex problems or concepts, we use flowcharts, sketches, and diagrams to aid our thought process. Leveraging this inherent ability ca
Externí odkaz:
http://arxiv.org/abs/2311.09241
The pre-trained large language models (LLMs) have shown their extraordinary capacity to solve reasoning tasks, even on tasks that require a complex process involving multiple sub-steps. However, given the vast possible generation space of all the tas
Externí odkaz:
http://arxiv.org/abs/2310.05452
Autor:
Tang, Xiaojuan, Zheng, Zilong, Li, Jiaqi, Meng, Fanxu, Zhu, Song-Chun, Liang, Yitao, Zhang, Muhan
The emergent few-shot reasoning capabilities of Large Language Models (LLMs) have excited the natural language and machine learning community over recent years. Despite of numerous successful applications, the underlying mechanism of such in-context
Externí odkaz:
http://arxiv.org/abs/2305.14825
Publikováno v:
In Energy Conversion and Management 15 August 2024 314
Publikováno v:
In Biochemical Engineering Journal August 2024 208
Although residual connection enables training very deep neural networks, it is not friendly for online inference due to its multi-branch topology. This encourages many researchers to work on designing DNNs without residual connections at inference. F
Externí odkaz:
http://arxiv.org/abs/2111.00687
Publikováno v:
In iScience 17 May 2024 27(5)