Zobrazeno 1 - 10
of 4 349
pro vyhledávání: '"Bao, Jie"'
Dense Retrieval (DR) is now considered as a promising tool to enhance the memorization capacity of Large Language Models (LLM) such as GPT3 and GPT-4 by incorporating external memories. However, due to the paradigm discrepancy between text generation
Externí odkaz:
http://arxiv.org/abs/2403.01999
We report observations of discrete charge states of a coherent dielectric two-level system (TLS) that is strongly coupled to an offset-charge-sensitive superconducting transmon qubit. We measure an offset charge of 0.072$e$ associated with the two TL
Externí odkaz:
http://arxiv.org/abs/2401.12183
Publikováno v:
Chemical Engineering Science, Vol. 221 115693 2020
Liquid holdup and mass transfer area are critical parameters for packed column design and CO2 capture efficiency prediction. In this paper, a framework was established for modeling the liquid-gas countercurrent flow hydrodynamics in a random packed c
Externí odkaz:
http://arxiv.org/abs/2310.19808
Autor:
Xinxin Gao, Ze Gu, Qian Ma, Bao Jie Chen, Kam-Man Shum, Wen Yi Cui, Jian Wei You, Tie Jun Cui, Chi Hou Chan
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-11 (2024)
Abstract All-optical diffractive neural networks, as analog artificial intelligence accelerators, leverage parallelism and analog computation for complex data processing. However, their low space transmission efficiency or large spatial dimensions hi
Externí odkaz:
https://doaj.org/article/da6e709f73f54ac69235475c4c9a5081
Autor:
Sun, Si, Lu, Yida, Yu, Shi, Li, Xiangyang, Li, Zhonghua, Cao, Zhao, Liu, Zhiyuan, Ye, Deiming, Bao, Jie
Few-shot dense retrieval (DR) aims to effectively generalize to novel search scenarios by learning a few samples. Despite its importance, there is little study on specialized datasets and standardized evaluation protocols. As a result, current method
Externí odkaz:
http://arxiv.org/abs/2304.05845
Autor:
Bartoldson, Brian R., Hu, Yeping, Saini, Amar, Cadena, Jose, Fu, Yucheng, Bao, Jie, Xu, Zhijie, Ng, Brenda, Nguyen, Phan
Data-driven modeling approaches can produce fast surrogates to study large-scale physics problems. Among them, graph neural networks (GNNs) that operate on mesh-based data are desirable because they possess inductive biases that promote physical fait
Externí odkaz:
http://arxiv.org/abs/2304.00338
Publikováno v:
IEEE Transactions on Automatic Control, 2023
This paper presents a distributed data-driven predictive control (DDPC) approach using the behavioral framework. It aims to design a network of controllers for an interconnected system with linear time-invariant (LTI) subsystems such that a given glo
Externí odkaz:
http://arxiv.org/abs/2303.00251