Zobrazeno 1 - 10
of 57
pro vyhledávání: '"Zhu, Qunxi"'
Complex systems in physics, chemistry, and biology that evolve over time with inherent randomness are typically described by stochastic differential equations (SDEs). A fundamental challenge in science and engineering is to determine the governing eq
Externí odkaz:
http://arxiv.org/abs/2410.16694
Accurately finding and predicting dynamics based on the observational data with noise perturbations is of paramount significance but still a major challenge presently. Here, for the Hamiltonian mechanics, we propose the Hamiltonian Neural Koopman Ope
Externí odkaz:
http://arxiv.org/abs/2406.02154
Autor:
Zhu, Qunxi, Lin, Wei
Continuous-time generative models, such as Flow Matching (FM), construct probability paths to transport between one distribution and another through the simulation-free learning of the neural ordinary differential equations (ODEs). During inference,
Externí odkaz:
http://arxiv.org/abs/2405.11605
Modeling complex systems using standard neural ordinary differential equations (NODEs) often faces some essential challenges, including high computational costs and susceptibility to local optima. To address these challenges, we propose a simulation-
Externí odkaz:
http://arxiv.org/abs/2405.11542
In order to stabilize nonlinear systems modeled by stochastic differential equations, we design a Fast Exponentially Stable and Safe Neural Controller (FESSNC) for fast learning controllers. Our framework is parameterized by neural networks, and real
Externí odkaz:
http://arxiv.org/abs/2405.11406
Aspect-Based Sentiment Analysis (ABSA) stands as a crucial task in predicting the sentiment polarity associated with identified aspects within text. However, a notable challenge in ABSA lies in precisely determining the aspects' boundaries (start and
Externí odkaz:
http://arxiv.org/abs/2402.15289
Autor:
Xiong, Limao, Zhou, Jie, Zhu, Qunxi, Wang, Xiao, Wu, Yuanbin, Zhang, Qi, Gui, Tao, Huang, Xuanjing, Ma, Jin, Shan, Ying
Existing models for named entity recognition (NER) are mainly based on large-scale labeled datasets, which always obtain using crowdsourcing. However, it is hard to obtain a unified and correct label via majority voting from multiple annotators for N
Externí odkaz:
http://arxiv.org/abs/2305.12485
Neural Ordinary Differential Equations (NODEs), a framework of continuous-depth neural networks, have been widely applied, showing exceptional efficacy in coping with representative datasets. Recently, an augmented framework has been developed to ove
Externí odkaz:
http://arxiv.org/abs/2304.05310
Control problems are always challenging since they arise from the real-world systems where stochasticity and randomness are of ubiquitous presence. This naturally and urgently calls for developing efficient neural control policies for stabilizing not
Externí odkaz:
http://arxiv.org/abs/2209.07240
Continuous-depth neural networks, such as the Neural Ordinary Differential Equations (ODEs), have aroused a great deal of interest from the communities of machine learning and data science in recent years, which bridge the connection between deep neu
Externí odkaz:
http://arxiv.org/abs/2201.00960