Zobrazeno 1 - 10
of 20 221
pro vyhledávání: '"WANG, Nan"'
Autor:
Sturua, Saba, Mohr, Isabelle, Akram, Mohammad Kalim, Günther, Michael, Wang, Bo, Krimmel, Markus, Wang, Feng, Mastrapas, Georgios, Koukounas, Andreas, Wang, Nan, Xiao, Han
We introduce jina-embeddings-v3, a novel text embedding model with 570 million parameters, achieves state-of-the-art performance on multilingual data and long-context retrieval tasks, supporting context lengths of up to 8192 tokens. The model include
Externí odkaz:
http://arxiv.org/abs/2409.10173
Autor:
Jha, Rohan, Wang, Bo, Günther, Michael, Mastrapas, Georgios, Sturua, Saba, Mohr, Isabelle, Koukounas, Andreas, Akram, Mohammad Kalim, Wang, Nan, Xiao, Han
Multi-vector dense models, such as ColBERT, have proven highly effective in information retrieval. ColBERT's late interaction scoring approximates the joint query-document attention seen in cross-encoders while maintaining inference efficiency closer
Externí odkaz:
http://arxiv.org/abs/2408.16672
Autor:
Shi, Lei, Liu, Zhimeng, Yang, Yi, Wu, Weize, Zhang, Yuyang, Zhang, Hongbo, Lin, Jing, Wu, Siyu, Chen, Zihan, Li, Ruiming, Wang, Nan, Liu, Zipeng, Tan, Huobin, Gao, Hongyi, Zhang, Yue, Wang, Ge
The extraction of Metal-Organic Frameworks (MOFs) synthesis conditions from literature text has been challenging but crucial for the logical design of new MOFs with desirable functionality. The recent advent of large language models (LLMs) provides d
Externí odkaz:
http://arxiv.org/abs/2408.04665
Autor:
Sun, Yanwen, Chen, Chaobo, Albert, Thies J., Li, Haoyuan, Arefev, Mikhail I., Chen, Ying, Dunne, Mike, Glownia, James M., Hoffmann, Matthias, Hurley, Matthew J., Mo, Mianzhen, Nguyen, Quynh L., Sato, Takahiro, Song, Sanghoon, Sun, Peihao, Sutton, Mark, Teitelbaum, Samuel, Valavanis, Antonios S., Wang, Nan, Zhu, Diling, Zhigilei, Leonid V., Sokolowski-Tinten, Klaus
Femtosecond laser ablation is a process that bears both fundamental physics interest and has wide industrial applications. For decades, the lack of probes on the relevant time and length scales has prevented access to the highly nonequilibrium phase
Externí odkaz:
http://arxiv.org/abs/2407.10505
Recent studies have demonstrated the effectiveness of using large language language models (LLMs) in passage ranking. The listwise approaches, such as RankGPT, have become new state-of-the-art in this task. However, the efficiency of RankGPT models i
Externí odkaz:
http://arxiv.org/abs/2406.14848
Federated Edge Learning (FEEL) emerges as a pioneering distributed machine learning paradigm for the 6G Hyper-Connectivity, harnessing data from the Internet of Things (IoT) devices while upholding data privacy. However, current FEEL algorithms strug
Externí odkaz:
http://arxiv.org/abs/2406.09776
Autor:
Chen, Danpeng, Li, Hai, Ye, Weicai, Wang, Yifan, Xie, Weijian, Zhai, Shangjin, Wang, Nan, Liu, Haomin, Bao, Hujun, Zhang, Guofeng
Recently, 3D Gaussian Splatting (3DGS) has attracted widespread attention due to its high-quality rendering, and ultra-fast training and rendering speed. However, due to the unstructured and irregular nature of Gaussian point clouds, it is difficult
Externí odkaz:
http://arxiv.org/abs/2406.06521
Autor:
Wang, Nan, Sanfelice, Ricardo G.
This paper focuses on the motion planning problem for the systems exhibiting both continuous and discrete behaviors, which we refer to as hybrid dynamical systems. Firstly, the motion planning problem for hybrid systems is formulated using the hybrid
Externí odkaz:
http://arxiv.org/abs/2406.01802
Autor:
Xu, Fan, Wang, Nan, Wu, Hao, Wen, Xuezhi, Zhang, Dalin, Lu, Siyang, Li, Binyong, Gong, Wei, Wan, Hai, Zhao, Xibin
Graph-Level Anomaly Detection (GLAD) aims to distinguish anomalous graphs within a graph dataset. However, current methods are constrained by their receptive fields, struggling to learn global features within the graphs. Moreover, most contemporary m
Externí odkaz:
http://arxiv.org/abs/2406.00734
Autor:
Koukounas, Andreas, Mastrapas, Georgios, Günther, Michael, Wang, Bo, Martens, Scott, Mohr, Isabelle, Sturua, Saba, Akram, Mohammad Kalim, Martínez, Joan Fontanals, Ognawala, Saahil, Guzman, Susana, Werk, Maximilian, Wang, Nan, Xiao, Han
Contrastive Language-Image Pretraining (CLIP) is widely used to train models to align images and texts in a common embedding space by mapping them to fixed-sized vectors. These models are key to multimodal information retrieval and related tasks. How
Externí odkaz:
http://arxiv.org/abs/2405.20204