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pro vyhledávání: '"FAN, Bin"'
PyPM is a Python-based domain specific language (DSL) for building rewrite-based optimization passes on machine learning computation graphs. Users define individual optimizations by writing (a) patterns that match subgraphs of a computation graph and
Externí odkaz:
http://arxiv.org/abs/2412.13398
Gaze object prediction (GOP) aims to predict the category and location of the object that a human is looking at. Previous methods utilized box-level supervision to identify the object that a person is looking at, but struggled with semantic ambiguity
Externí odkaz:
http://arxiv.org/abs/2408.01044
Autor:
Tang, Chunxu, Fan, Bin, Zhao, Jing, Liang, Chen, Wang, Yi, Wang, Beinan, Qiu, Ziyue, Qiu, Lu, Ding, Bowen, Sun, Shouzhuo, Che, Saiguang, Mai, Jiaming, Chen, Shouwei, Zhu, Yu, Xie, Jianjian, Yutian, Sun, Li, Yao, Zhang, Yangjun, Wang, Ke, Chen, Mingmin
With the exponential growth of data and evolving use cases, petabyte-scale OLAP data platforms are increasingly adopting a model that decouples compute from storage. This shift, evident in organizations like Uber and Meta, introduces operational chal
Externí odkaz:
http://arxiv.org/abs/2406.05962
Autor:
Zhang, Chenghao, Meng, Gaofeng, Fan, Bin, Tian, Kun, Zhang, Zhaoxiang, Xiang, Shiming, Pan, Chunhong
The remarkable performance of recent stereo depth estimation models benefits from the successful use of convolutional neural networks to regress dense disparity. Akin to most tasks, this needs gathering training data that covers a number of heterogen
Externí odkaz:
http://arxiv.org/abs/2404.00360
Accurate motion prediction of pedestrians, cyclists, and other surrounding vehicles (all called agents) is very important for autonomous driving. Most existing works capture map information through an one-stage interaction with map by vector-based at
Externí odkaz:
http://arxiv.org/abs/2403.16374
We observe a high level of imbalance in the accuracy of different classes in the same old task for the first time. This intriguing phenomenon, discovered in replay-based Class Incremental Learning (CIL), highlights the imbalanced forgetting of learne
Externí odkaz:
http://arxiv.org/abs/2403.14910
Autor:
Tang, Chunxu, Wang, Yi, Fan, Bin, Wang, Beinan, Chen, Shouwei, Qiu, Ziyue, Liang, Chen, Zhao, Jing, Zhu, Yu, Chen, Mingmin, Hu, Zhongting
This paper explores a prevailing trend in the industry: migrating data-intensive analytics applications from on-premises to cloud-native environments. We find that the unique cost models associated with cloud-based storage necessitate a more nuanced
Externí odkaz:
http://arxiv.org/abs/2311.00156
Autor:
Fan, Bin
In this paper, we consider an inverse space-dependent source problem for a time-fractional diffusion equation. To deal with the ill-posedness of the problem, we transform the problem into an optimal control problem with total variational (TV) regular
Externí odkaz:
http://arxiv.org/abs/2310.12029
Autor:
Fan, Bin
Publikováno v:
AIMS Mathematics 2024 9(3)
In this paper, we consider a numerical method for the multi-term Caputo-Fabrizio time-fractional diffusion equations (with orders $\alpha_i\in(0,1)$, $i=1,2,\cdots,n$). The proposed method employs a fast finite difference scheme to approximate multi-
Externí odkaz:
http://arxiv.org/abs/2307.08078