Zobrazeno 1 - 10
of 5 584
pro vyhledávání: '"Zhang,Jingyu"'
Autor:
Zhang, Jingyu, Wang, Yilei, Qian, Lang, Sun, Peng, Li, Zengwen, Jiang, Sudong, Liu, Maolin, Song, Liang
As a potential application of Vehicle-to-Everything (V2X) communication, multi-agent collaborative perception has achieved significant success in 3D object detection. While these methods have demonstrated impressive results on standard benchmarks, th
Externí odkaz:
http://arxiv.org/abs/2412.10739
The current paradigm for safety alignment of large language models (LLMs) follows a one-size-fits-all approach: the model refuses to interact with any content deemed unsafe by the model provider. This approach lacks flexibility in the face of varying
Externí odkaz:
http://arxiv.org/abs/2410.08968
Autor:
Jiang, Dongwei, Wang, Guoxuan, Lu, Yining, Wang, Andrew, Zhang, Jingyu, Liu, Chuyu, Van Durme, Benjamin, Khashabi, Daniel
The reasoning steps generated by LLMs might be incomplete, as they mimic logical leaps common in everyday communication found in their pre-training data: underlying rationales are frequently left implicit (unstated). To address this challenge, we int
Externí odkaz:
http://arxiv.org/abs/2410.01044
It is very important to detect traffic signs efficiently and accurately in autonomous driving systems. However, the farther the distance, the smaller the traffic signs. Existing object detection algorithms can hardly detect these small scaled signs.I
Externí odkaz:
http://arxiv.org/abs/2409.03320
The recent rise of EEG-based end-to-end deep learning models presents a significant challenge in elucidating how these models process raw EEG signals and generate predictions in the frequency domain. This challenge limits the transparency and credibi
Externí odkaz:
http://arxiv.org/abs/2407.17983
Autor:
Jiang, Zhengping, Zhang, Jingyu, Weir, Nathaniel, Ebner, Seth, Wanner, Miriam, Sanders, Kate, Khashabi, Daniel, Liu, Anqi, Van Durme, Benjamin
Hallucinations pose a challenge to the application of large language models (LLMs) thereby motivating the development of metrics to evaluate factual precision. We observe that popular metrics using the Decompose-Then-Verify framework, such as \FActSc
Externí odkaz:
http://arxiv.org/abs/2407.03572
With the widespread application of Light Detection and Ranging (LiDAR) technology in fields such as autonomous driving, robot navigation, and terrain mapping, the importance of edge detection in LiDAR images has become increasingly prominent. Traditi
Externí odkaz:
http://arxiv.org/abs/2406.09773
This paper explores the application of Natural Language Processing (NLP) in financial risk detection. By constructing an NLP-based financial risk detection model, this study aims to identify and predict potential risks in financial documents and comm
Externí odkaz:
http://arxiv.org/abs/2406.09765
This research aims to explore the application of deep learning in autonomous driving computer vision technology and its impact on improving system performance. By using advanced technologies such as convolutional neural networks (CNN), multi-task joi
Externí odkaz:
http://arxiv.org/abs/2406.00490
Non-autoregressive Transformers (NATs) are recently applied in direct speech-to-speech translation systems, which convert speech across different languages without intermediate text data. Although NATs generate high-quality outputs and offer faster i
Externí odkaz:
http://arxiv.org/abs/2405.13274