Zobrazeno 1 - 10
of 73
pro vyhledávání: '"Ling, Zenan"'
Diffusion models have risen to prominence in time series forecasting, showcasing their robust capability to model complex data distributions. However, their effectiveness in deterministic predictions is often constrained by instability arising from t
Externí odkaz:
http://arxiv.org/abs/2411.04491
Implicit graph neural networks (IGNNs), which exhibit strong expressive power with a single layer, have recently demonstrated remarkable performance in capturing long-range dependencies (LRD) in underlying graphs while effectively mitigating the over
Externí odkaz:
http://arxiv.org/abs/2410.08524
Existing permanental processes often impose constraints on kernel types or stationarity, limiting the model's expressiveness. To overcome these limitations, we propose a novel approach utilizing the sparse spectral representation of nonstationary ker
Externí odkaz:
http://arxiv.org/abs/2410.03581
Autor:
Wang, Fuhai, Huang, Yunlong, Feng, Zhanbo, Xiong, Rujing, Li, Zhe, Wang, Chun, Mi, Tiebin, Qiu, Robert Caiming, Ling, Zenan
Reconfigurable intelligent surfaces (RISs) have emerged as a promising auxiliary technology for radio frequency imaging. However, existing works face challenges of faint and intricate back-scattered waves and the restricted field-of-view (FoV), both
Externí odkaz:
http://arxiv.org/abs/2407.14820
Recently, ray tracing has gained renewed interest with the advent of Reflective Intelligent Surfaces (RIS) technology, a key enabler of 6G wireless communications due to its capability of intelligent manipulation of electromagnetic waves. However, ac
Externí odkaz:
http://arxiv.org/abs/2405.11541
Autor:
Ling, Zenan, Li, Longbo, Feng, Zhanbo, Zhang, Yixuan, Zhou, Feng, Qiu, Robert C., Liao, Zhenyu
Deep equilibrium models (DEQs), as a typical implicit neural network, have demonstrated remarkable success on various tasks. There is, however, a lack of theoretical understanding of the connections and differences between implicit DEQs and explicit
Externí odkaz:
http://arxiv.org/abs/2402.02697
Discrimination can occur when the underlying unbiased labels are overwritten by an agent with potential bias, resulting in biased datasets that unfairly harm specific groups and cause classifiers to inherit these biases. In this paper, we demonstrate
Externí odkaz:
http://arxiv.org/abs/2312.08749
Meta-learning has demonstrated promising results in few-shot classification (FSC) by learning to solve new problems using prior knowledge. Bayesian methods are effective at characterizing uncertainty in FSC, which is crucial in high-risk fields. In t
Externí odkaz:
http://arxiv.org/abs/2310.10379
Denoising diffusion models have shown outstanding performance in image editing. Existing works tend to use either image-guided methods, which provide a visual reference but lack control over semantic coherence, or text-guided methods, which ensure fa
Externí odkaz:
http://arxiv.org/abs/2308.15854
Implicit neural networks have demonstrated remarkable success in various tasks. However, there is a lack of theoretical analysis of the connections and differences between implicit and explicit networks. In this paper, we study high-dimensional impli
Externí odkaz:
http://arxiv.org/abs/2308.16425