Zobrazeno 1 - 10
of 125
pro vyhledávání: '"Wan, Lipeng"'
Effective exploration is crucial to discovering optimal strategies for multi-agent reinforcement learning (MARL) in complex coordination tasks. Existing methods mainly utilize intrinsic rewards to enable committed exploration or use role-based learni
Externí odkaz:
http://arxiv.org/abs/2402.17978
MGARD: A multigrid framework for high-performance, error-controlled data compression and refactoring
Autor:
Gong, Qian, Chen, Jieyang, Whitney, Ben, Liang, Xin, Reshniak, Viktor, Banerjee, Tania, Lee, Jaemoon, Rangarajan, Anand, Wan, Lipeng, Vidal, Nicolas, Liu, Qing, Gainaru, Ana, Podhorszki, Norbert, Archibald, Richard, Ranka, Sanjay, Klasky, Scott
Publikováno v:
SoftwareX, 24(2023), 101590
We describe MGARD, a software providing MultiGrid Adaptive Reduction for floating-point scientific data on structured and unstructured grids. With exceptional data compression capability and precise error control, MGARD addresses a wide range of requ
Externí odkaz:
http://arxiv.org/abs/2401.05994
Autor:
Gong, Qian, Zhang, Chengzhu, Liang, Xin, Reshniak, Viktor, Chen, Jieyang, Rangarajan, Anand, Ranka, Sanjay, Vidal, Nicolas, Wan, Lipeng, Ullrich, Paul, Podhorszki, Norbert, Jacob, Robert, Klasky, Scott
Publikováno v:
2023 IEEE 19th International Conference on e-Science, Limassol, Cyprus, 2023, pp. 1-10
Scientific discoveries are increasingly constrained by limited storage space and I/O capacities. For time-series simulations and experiments, their data often need to be decimated over timesteps to accommodate storage and I/O limitations. In this pap
Externí odkaz:
http://arxiv.org/abs/2401.03317
Autor:
Leng, Haoju, Deng, Ruining, Bao, Shunxing, Fang, Dazheng, Millis, Bryan A., Tang, Yucheng, Yang, Haichun, Wang, Xiao, Peng, Yifan, Wan, Lipeng, Huo, Yuankai
When dealing with giga-pixel digital pathology in whole-slide imaging, a notable proportion of data records holds relevance during each analysis operation. For instance, when deploying an image analysis algorithm on whole-slide images (WSI), the comp
Externí odkaz:
http://arxiv.org/abs/2308.05784
Autor:
Leng, Haoju, Deng, Ruining, Asad, Zuhayr, Womick, R. Michael, Yang, Haichun, Wan, Lipeng, Huo, Yuankai
Deep-learning techniques have been used widely to alleviate the labour-intensive and time-consuming manual annotation required for pixel-level tissue characterization. Our previous study introduced an efficient single dynamic network - Omni-Seg - tha
Externí odkaz:
http://arxiv.org/abs/2305.14566
Manipulation relationship detection (MRD) aims to guide the robot to grasp objects in the right order, which is important to ensure the safety and reliability of grasping in object stacked scenes. Previous works infer manipulation relationship by dee
Externí odkaz:
http://arxiv.org/abs/2304.12592
The superposition of several optical beams with large mutual angles results in sub-micrometer periodic patterns with a complex intensity, phase and polarization structure. For high-resolution imaging thereof, one often employs optical super-resolutio
Externí odkaz:
http://arxiv.org/abs/2212.09373
Due to the representation limitation of the joint Q value function, multi-agent reinforcement learning methods with linear value decomposition (LVD) or monotonic value decomposition (MVD) suffer from relative overgeneralization. As a result, they can
Externí odkaz:
http://arxiv.org/abs/2211.12075
Due to the representation limitation of the joint Q value function, multi-agent reinforcement learning methods with linear value decomposition (LVD) or monotonic value decomposition (MVD) suffer from relative overgeneralization. As a result, they can
Externí odkaz:
http://arxiv.org/abs/2112.04454
Intention sharing is crucial for efficient cooperation under partially observable environments in multi-agent reinforcement learning (MARL). However, message deceiving, i.e., a mismatch between the propagated intentions and the final decisions, may h
Externí odkaz:
http://arxiv.org/abs/2112.01078