Zobrazeno 1 - 10
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pro vyhledávání: '"Chen, Xiaohui"'
Autor:
Chen, Xiaohui, Li, Hui
For an integral domain $R$ satisfying certain condition, we characterize the primitive ideal space and its Jacobson topology of the semigroup crossed product $C^*(R_+) \rtimes R^\times$. The main example is when $R=\mathbb{Z}[\sqrt{-3}]$.
Externí odkaz:
http://arxiv.org/abs/2408.09863
Document-level relation extraction (DocRE) aims to extract relations between entities from unstructured document text. Compared to sentence-level relation extraction, it requires more complex semantic understanding from a broader text context. Curren
Externí odkaz:
http://arxiv.org/abs/2407.21384
Motivated by approximation Bayesian computation using mean-field variational approximation and the computation of equilibrium in multi-species systems with cross-interaction, this paper investigates the composite geodesically convex optimization prob
Externí odkaz:
http://arxiv.org/abs/2405.04628
Autor:
Chen, Xiaohui, Luo, Tie
In the field of Medical Imaging, extensive research has been dedicated to leveraging its potential in uncovering critical diagnostic features in patients. Artificial Intelligence (AI)-driven medical diagnosis relies on sophisticated machine learning
Externí odkaz:
http://arxiv.org/abs/2402.16005
Humanoid robots are designed to be relatable to humans for applications such as customer support and helpdesk services. However, many such systems, including Softbank's Pepper, fall short because they fail to communicate effectively with humans. The
Externí odkaz:
http://arxiv.org/abs/2402.07095
The emergence of neural networks constrained by physical governing equations has sparked a new trend in deep learning research, which is known as Physics-Informed Neural Networks (PINNs). However, solving high-dimensional problems with PINNs is still
Externí odkaz:
http://arxiv.org/abs/2401.05439
Many deep learning based automated medical image segmentation systems, in reality, face difficulties in deployment due to the cost of massive data annotation and high latency in model iteration. We propose a dynamic interactive learning framework tha
Externí odkaz:
http://arxiv.org/abs/2312.06072
Recently developed deep neural models like NetGAN, CELL, and Variational Graph Autoencoders have made progress but face limitations in replicating key graph statistics on generating large graphs. Diffusion-based methods have emerged as promising alte
Externí odkaz:
http://arxiv.org/abs/2310.14441
Publikováno v:
Phys. Rev. Lett. 132, 100602 (2024)
Deep generative models are key-enabling technology to computer vision, text generation, and large language models. Denoising diffusion probabilistic models (DDPMs) have recently gained much attention due to their ability to generate diverse and high-
Externí odkaz:
http://arxiv.org/abs/2310.05866
Self-supervised training methods for transformers have demonstrated remarkable performance across various domains. Previous transformer-based models, such as masked autoencoders (MAE), typically utilize a single normalization layer for both the [CLS]
Externí odkaz:
http://arxiv.org/abs/2309.12931