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pro vyhledávání: '"Junyi Han'
Autor:
Ren, Tianhe, Chen, Yihao, Jiang, Qing, Zeng, Zhaoyang, Xiong, Yuda, Liu, Wenlong, Ma, Zhengyu, Shen, Junyi, Gao, Yuan, Jiang, Xiaoke, Chen, Xingyu, Song, Zhuheng, Zhang, Yuhong, Huang, Hongjie, Gao, Han, Liu, Shilong, Zhang, Hao, Li, Feng, Yu, Kent, Zhang, Lei
In this paper, we introduce DINO-X, which is a unified object-centric vision model developed by IDEA Research with the best open-world object detection performance to date. DINO-X employs the same Transformer-based encoder-decoder architecture as Gro
Externí odkaz:
http://arxiv.org/abs/2411.14347
Akademický článek
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Autor:
Tang, Tianyi, Hu, Yiwen, Li, Bingqian, Luo, Wenyang, Qin, Zijing, Sun, Haoxiang, Wang, Jiapeng, Xu, Shiyi, Cheng, Xiaoxue, Guo, Geyang, Peng, Han, Zheng, Bowen, Tang, Yiru, Min, Yingqian, Chen, Yushuo, Chen, Jie, Zhao, Yuanqian, Ding, Luran, Wang, Yuhao, Dong, Zican, Xia, Chunxuan, Li, Junyi, Zhou, Kun, Zhao, Wayne Xin, Wen, Ji-Rong
To facilitate the research on large language models (LLMs), this paper presents a comprehensive and unified library, LLMBox, to ease the development, use, and evaluation of LLMs. This library is featured with three main merits: (1) a unified data int
Externí odkaz:
http://arxiv.org/abs/2407.05563
Autor:
Fan, Junyi, Han, Yuxuan, Zeng, Jialin, Cai, Jian-Feng, Wang, Yang, Xiang, Yang, Zhang, Jiheng
Efficiently learning equilibria with large state and action spaces in general-sum Markov games while overcoming the curse of multi-agency is a challenging problem. Recent works have attempted to solve this problem by employing independent linear func
Externí odkaz:
http://arxiv.org/abs/2403.11544
Autor:
Lee, Jonathan W., Wang, Han, Jang, Kathy, Hayat, Amaury, Bunting, Matthew, Alanqary, Arwa, Barbour, William, Fu, Zhe, Gong, Xiaoqian, Gunter, George, Hornstein, Sharon, Kreidieh, Abdul Rahman, Lichtlé, Nathan, Nice, Matthew W., Richardson, William A., Shah, Adit, Vinitsky, Eugene, Wu, Fangyu, Xiang, Shengquan, Almatrudi, Sulaiman, Althukair, Fahd, Bhadani, Rahul, Carpio, Joy, Chekroun, Raphael, Cheng, Eric, Chiri, Maria Teresa, Chou, Fang-Chieh, Delorenzo, Ryan, Gibson, Marsalis, Gloudemans, Derek, Gollakota, Anish, Ji, Junyi, Keimer, Alexander, Khoudari, Nour, Mahmood, Malaika, Mahmood, Mikail, Matin, Hossein Nick Zinat, Mcquade, Sean, Ramadan, Rabie, Urieli, Daniel, Wang, Xia, Wang, Yanbing, Xu, Rita, Yao, Mengsha, You, Yiling, Zachár, Gergely, Zhao, Yibo, Ameli, Mostafa, Baig, Mirza Najamuddin, Bhaskaran, Sarah, Butts, Kenneth, Gowda, Manasi, Janssen, Caroline, Lee, John, Pedersen, Liam, Wagner, Riley, Zhang, Zimo, Zhou, Chang, Work, Daniel B., Seibold, Benjamin, Sprinkle, Jonathan, Piccoli, Benedetto, Monache, Maria Laura Delle, Bayen, Alexandre M.
The CIRCLES project aims to reduce instabilities in traffic flow, which are naturally occurring phenomena due to human driving behavior. These "phantom jams" or "stop-and-go waves,"are a significant source of wasted energy. Toward this goal, the CIRC
Externí odkaz:
http://arxiv.org/abs/2402.17043
Large-scale 3D scene reconstruction and novel view synthesis are vital for autonomous vehicles, especially utilizing temporally sparse LiDAR frames. However, conventional explicit representations remain a significant bottleneck towards representing t
Externí odkaz:
http://arxiv.org/abs/2402.09325
Autor:
Xiong, Zhangyang, Li, Chenghong, Liu, Kenkun, Liao, Hongjie, Hu, Jianqiao, Zhu, Junyi, Ning, Shuliang, Qiu, Lingteng, Wang, Chongjie, Wang, Shijie, Cui, Shuguang, Han, Xiaoguang
In this era, the success of large language models and text-to-image models can be attributed to the driving force of large-scale datasets. However, in the realm of 3D vision, while remarkable progress has been made with models trained on large-scale
Externí odkaz:
http://arxiv.org/abs/2312.02963
Autor:
Yang, Jianlei, Liao, Jiacheng, Lei, Fanding, Liu, Meichen, Chen, Junyi, Long, Lingkun, Wan, Han, Yu, Bei, Zhao, Weisheng
Developing deep learning models on tiny devices (e.g. Microcontroller units, MCUs) has attracted much attention in various embedded IoT applications. However, it is challenging to efficiently design and deploy recent advanced models (e.g. transformer
Externí odkaz:
http://arxiv.org/abs/2311.01759
Reconstructing large-scale 3D scenes is essential for autonomous vehicles, especially when partial sensor data is lost. Although the recently developed neural radiance fields (NeRF) have shown compelling results in implicit representations, the large
Externí odkaz:
http://arxiv.org/abs/2310.00874
In this paper, we study a continuous-time exploratory mean-variance (EMV) problem under the framework of reinforcement learning (RL), and the Choquet regularizers are used to measure the level of exploration. By applying the classical Bellman princip
Externí odkaz:
http://arxiv.org/abs/2307.03026