Zobrazeno 1 - 10
of 3 114
pro vyhledávání: '"WU, Zhe"'
Autor:
Ying, Jiacheng, Liu, Mushui, Wu, Zhe, Zhang, Runming, Yu, Zhu, Fu, Siming, Cao, Si-Yuan, Wu, Chao, Yu, Yunlong, Shen, Hui-Liang
Blind face restoration has made great progress in producing high-quality and lifelike images. Yet it remains challenging to preserve the ID information especially when the degradation is heavy. Current reference-guided face restoration approaches eit
Externí odkaz:
http://arxiv.org/abs/2411.14125
Autor:
Saadany, Hadeel, Bhosale, Swapnil, Agrawal, Samarth, Kanojia, Diptesh, Orasan, Constantin, Wu, Zhe
This paper addresses the challenge of improving user experience on e-commerce platforms by enhancing product ranking relevant to users' search queries. Ambiguity and complexity of user queries often lead to a mismatch between the user's intent and re
Externí odkaz:
http://arxiv.org/abs/2410.15930
The emergence of Large Language Models (LLMs) has revolutionized natural language processing in various applications especially in e-commerce. One crucial step before the application of such LLMs in these fields is to understand and compare the perfo
Externí odkaz:
http://arxiv.org/abs/2408.12779
Time series forecasting is a crucial task that predicts the future values of variables based on historical data. Time series forecasting techniques have been developing in parallel with the machine learning community, from early statistical learning
Externí odkaz:
http://arxiv.org/abs/2408.11306
Explicit machine learning-based model predictive control (explicit ML-MPC) has been developed to reduce the real-time computational demands of traditional ML-MPC. However, the evaluation of candidate control actions in explicit ML-MPC can be time-con
Externí odkaz:
http://arxiv.org/abs/2408.06580
Proximal Policy Optimization (PPO) is a popular model-free reinforcement learning algorithm, esteemed for its simplicity and efficacy. However, due to its inherent on-policy nature, its proficiency in harnessing data from disparate policies is constr
Externí odkaz:
http://arxiv.org/abs/2406.03894
On-policy reinforcement learning methods, like Trust Region Policy Optimization (TRPO) and Proximal Policy Optimization (PPO), often demand extensive data per update, leading to sample inefficiency. This paper introduces Reflective Policy Optimizatio
Externí odkaz:
http://arxiv.org/abs/2406.03678
Autor:
Zhang, Xue, Cao, Si-Yuan, Wang, Fang, Zhang, Runmin, Wu, Zhe, Zhang, Xiaohan, Bai, Xiaokai, Shen, Hui-Liang
Most recent multispectral object detectors employ a two-branch structure to extract features from RGB and thermal images. While the two-branch structure achieves better performance than a single-branch structure, it overlooks inference efficiency. Th
Externí odkaz:
http://arxiv.org/abs/2405.16038
Autor:
Wang, Zihao, Wu, Zhe
In this work, we present a novel application of foundation models for chemical reactor modeling. Accurate modeling of real-world chemical reactors through first-principles is often challenging, and the process of rebuilding and retraining models for
Externí odkaz:
http://arxiv.org/abs/2405.11752
We present QueryNER, a manually-annotated dataset and accompanying model for e-commerce query segmentation. Prior work in sequence labeling for e-commerce has largely addressed aspect-value extraction which focuses on extracting portions of a product
Externí odkaz:
http://arxiv.org/abs/2405.09507