Zobrazeno 1 - 10
of 270
pro vyhledávání: '"Lin, Junhong"'
In multi-label classification, machine learning encounters the challenge of domain generalization when handling tasks with distributions differing from the training data. Existing approaches primarily focus on vision object recognition and neglect th
Externí odkaz:
http://arxiv.org/abs/2408.05831
Many real-world graphs frequently present challenges for graph learning due to the presence of both heterophily and heterogeneity. However, existing benchmarks for graph learning often focus on heterogeneous graphs with homophily or homogeneous graph
Externí odkaz:
http://arxiv.org/abs/2407.10916
In addition to low light, night images suffer degradation from light effects (e.g., glare, floodlight, etc). However, existing nighttime visibility enhancement methods generally focus on low-light regions, which neglects, or even amplifies the light
Externí odkaz:
http://arxiv.org/abs/2403.01083
Autor:
Hong, Yusu, Lin, Junhong
In this study, we revisit the convergence of AdaGrad with momentum (covering AdaGrad as a special case) on non-convex smooth optimization problems. We consider a general noise model where the noise magnitude is controlled by the function value gap to
Externí odkaz:
http://arxiv.org/abs/2402.13794
Autor:
Su, Jing, Jiang, Chufeng, Jin, Xin, Qiao, Yuxin, Xiao, Tingsong, Ma, Hongda, Wei, Rong, Jing, Zhi, Xu, Jiajun, Lin, Junhong
This systematic literature review comprehensively examines the application of Large Language Models (LLMs) in forecasting and anomaly detection, highlighting the current state of research, inherent challenges, and prospective future directions. LLMs
Externí odkaz:
http://arxiv.org/abs/2402.10350
Autor:
Sun, Tan, Lin, Junhong
Graph neural networks (GNNs) have gained popularity for various graph-related tasks. However, similar to deep neural networks, GNNs are also vulnerable to adversarial attacks. Empirical studies have shown that adversarially robust generalization has
Externí odkaz:
http://arxiv.org/abs/2402.04038
Autor:
Hong, Yusu, Lin, Junhong
The Adaptive Momentum Estimation (Adam) algorithm is highly effective in training various deep learning tasks. Despite this, there's limited theoretical understanding for Adam, especially when focusing on its vanilla form in non-convex smooth scenari
Externí odkaz:
http://arxiv.org/abs/2402.03982
We study deterministic matrix completion problem, i.e., recovering a low-rank matrix from a few observed entries where the sampling set is chosen as the edge set of a Ramanujan graph. We first investigate projected gradient descent (PGD) applied to a
Externí odkaz:
http://arxiv.org/abs/2401.06592
Autor:
Hong, Yusu, Lin, Junhong
In this paper, we study the convergence of the Adaptive Moment Estimation (Adam) algorithm under unconstrained non-convex smooth stochastic optimizations. Despite the widespread usage in machine learning areas, its theoretical properties remain limit
Externí odkaz:
http://arxiv.org/abs/2311.02000
Autor:
Lin, Junhong
Aged-related macular degeneration (AMD) and diabetic retinopathy (DR), the leading cause of significant vision loss worldwide, alter the retinal structure and capillary blood flow in eyes. Optical coherence tomography (OCT) and angiography (OCTA), th
Externí odkaz:
https://hdl.handle.net/1721.1/147445