Zobrazeno 1 - 10
of 125
pro vyhledávání: '"Sun, Xiuyu"'
Autor:
Sun, Xiuyu, Zhong, Xiaohui, Xu, Xiaoze, Huang, Yuanqing, Li, Hao, Feng, Jie, Han, Wei, Wu, Libo, Qi, Yuan
Operational numerical weather prediction systems consist of three fundamental components: the global observing system for data collection, data assimilation for generating initial conditions, and the forecasting model to predict future weather condit
Externí odkaz:
http://arxiv.org/abs/2408.05472
Autor:
Qian, Yichen, He, Yongyi, Zhu, Rong, Huang, Jintao, Ma, Zhijian, Wang, Haibin, Wang, Yaohua, Sun, Xiuyu, Lian, Defu, Ding, Bolin, Zhou, Jingren
Designing effective data manipulation methods is a long standing problem in data lakes. Traditional methods, which rely on rules or machine learning models, require extensive human efforts on training data collection and tuning models. Recent methods
Externí odkaz:
http://arxiv.org/abs/2405.06510
Data assimilation (DA), as an indispensable component within contemporary Numerical Weather Prediction (NWP) systems, plays a crucial role in generating the analysis that significantly impacts forecast performance. Nevertheless, the development of an
Externí odkaz:
http://arxiv.org/abs/2404.08522
Large language models (LLMs) have emerged as a new paradigm for Text-to-SQL task. However, the absence of a systematical benchmark inhibits the development of designing effective, efficient and economic LLM-based Text-to-SQL solutions. To address thi
Externí odkaz:
http://arxiv.org/abs/2308.15363
Knowledge distillation is an effective paradigm for boosting the performance of pocket-size model, especially when multiple teacher models are available, the student would break the upper limit again. However, it is not economical to train diverse te
Externí odkaz:
http://arxiv.org/abs/2305.02722
The wide application of pre-trained models is driving the trend of once-for-all training in one-shot neural architecture search (NAS). However, training within a huge sample space damages the performance of individual subnets and requires much comput
Externí odkaz:
http://arxiv.org/abs/2304.14636
Autor:
Chen, Weihua, Xu, Xianzhe, Jia, Jian, luo, Hao, Wang, Yaohua, Wang, Fan, Jin, Rong, Sun, Xiuyu
Human-centric visual tasks have attracted increasing research attention due to their widespread applications. In this paper, we aim to learn a general human representation from massive unlabeled human images which can benefit downstream human-centric
Externí odkaz:
http://arxiv.org/abs/2303.17602
The rapid advances in Vision Transformer (ViT) refresh the state-of-the-art performances in various vision tasks, overshadowing the conventional CNN-based models. This ignites a few recent striking-back research in the CNN world showing that pure CNN
Externí odkaz:
http://arxiv.org/abs/2303.02165
Autor:
Wang, Junyan, Sun, Zhenhong, Qian, Yichen, Gong, Dong, Sun, Xiuyu, Lin, Ming, Pagnucco, Maurice, Song, Yang
3D convolution neural networks (CNNs) have been the prevailing option for video recognition. To capture the temporal information, 3D convolutions are computed along the sequences, leading to cubically growing and expensive computations. To reduce the
Externí odkaz:
http://arxiv.org/abs/2303.02693
In this report, we present a fast and accurate object detection method dubbed DAMO-YOLO, which achieves higher performance than the state-of-the-art YOLO series. DAMO-YOLO is extended from YOLO with some new technologies, including Neural Architectur
Externí odkaz:
http://arxiv.org/abs/2211.15444