Zobrazeno 1 - 10
of 30 456
pro vyhledávání: '"P, Chee"'
Autor:
AlDahoul, Nouar, Tan, Myles Joshua Toledo, Tera, Raghava Reddy, Karim, Hezerul Abdul, Lim, Chee How, Mishra, Manish Kumar, Zaki, Yasir
License plate recognition (LPR) involves automated systems that utilize cameras and computer vision to read vehicle license plates. Such plates collected through LPR can then be compared against databases to identify stolen vehicles, uninsured driver
Externí odkaz:
http://arxiv.org/abs/2412.14197
Autor:
Feng, K. J. Kevin, Pu, Kevin, Latzke, Matt, August, Tal, Siangliulue, Pao, Bragg, Jonathan, Weld, Daniel S., Zhang, Amy X., Chang, Joseph Chee
We present Cocoa, a system that implements a novel interaction design pattern -- interactive plans -- for users to collaborate with an AI agent on complex, multi-step tasks in a document editor. Cocoa harmonizes human and AI efforts and enables flexi
Externí odkaz:
http://arxiv.org/abs/2412.10999
Autor:
Ng, Chee, Fung, Yuen
Large Vision-Language Models (LVLMs) have demonstrated remarkable performance across a wide range of multimodal tasks. However, fine-tuning these models for domain-specific applications remains a computationally intensive challenge. This paper introd
Externí odkaz:
http://arxiv.org/abs/2412.09875
Autor:
Sim, Chee-Khian
In this note, we reduce an instance of the partition problem to a dynamic lot sizing problem in polynomial time, and show that solving the latter problem solves the former problem. We further show that the instance of the partition problem can be sol
Externí odkaz:
http://arxiv.org/abs/2412.05017
The parameter space of freeze-in dark matter (DM) with mass $m_\chi$ through light dark photon (``minimal freeze-in DM'') is currently being probed by direct detection experiments through electron and nuclear recoil. Exploring the DM production in th
Externí odkaz:
http://arxiv.org/abs/2412.04550
Autor:
Yu, Zhuoyuan, Guo, Hongliang, Adiwahono, Albertus Hendrawan, Chan, Jianle, Tynn, Brina Shong Wey, Chew, Chee-Meng, Yau, Wei-Yun
This paper studies the multi-robot reliable navigation problem in uncertain topological networks, which aims at maximizing the robot team's on-time arrival probabilities in the face of road network uncertainties. The uncertainty in these networks ste
Externí odkaz:
http://arxiv.org/abs/2411.16134
Autor:
Asai, Akari, He, Jacqueline, Shao, Rulin, Shi, Weijia, Singh, Amanpreet, Chang, Joseph Chee, Lo, Kyle, Soldaini, Luca, Feldman, Sergey, D'arcy, Mike, Wadden, David, Latzke, Matt, Tian, Minyang, Ji, Pan, Liu, Shengyan, Tong, Hao, Wu, Bohao, Xiong, Yanyu, Zettlemoyer, Luke, Neubig, Graham, Weld, Dan, Downey, Doug, Yih, Wen-tau, Koh, Pang Wei, Hajishirzi, Hannaneh
Scientific progress depends on researchers' ability to synthesize the growing body of literature. Can large language models (LMs) assist scientists in this task? We introduce OpenScholar, a specialized retrieval-augmented LM that answers scientific q
Externí odkaz:
http://arxiv.org/abs/2411.14199
Quantum Convolutional Neural Networks (QCNNs) have emerged as promising models for quantum machine learning tasks, including classification and data compression. This paper investigates the performance of QCNNs in comparison to the hardware-efficient
Externí odkaz:
http://arxiv.org/abs/2411.13468
Federated unlearning (FU) offers a promising solution to effectively address the need to erase the impact of specific clients' data on the global model in federated learning (FL), thereby granting individuals the ``Right to be Forgotten". The most st
Externí odkaz:
http://arxiv.org/abs/2411.11044
Federated learning facilitates collaborative machine learning, enabling multiple participants to collectively develop a shared model while preserving the privacy of individual data. The growing importance of the "right to be forgotten" calls for effe
Externí odkaz:
http://arxiv.org/abs/2411.11039