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In machine learning and data mining, outliers are data points that significantly differ from the dataset and often introduce irrelevant information that can induce bias in its statistics and models. Therefore, unsupervised methods are crucial to dete
Externí odkaz:
http://arxiv.org/abs/2411.08867
Accurate estimates of causal effects play a key role in decision-making across applications such as healthcare, economics, and operations. In the absence of randomized experiments, a common approach to estimating causal effects uses \textit{covariate
Externí odkaz:
http://arxiv.org/abs/2411.08141
Diabetic Retinopathy (DR) is a primary cause of blindness, necessitating early detection and diagnosis. This paper focuses on referable DR classification to enhance the applicability of the proposed method in clinical practice. We develop an advanced
Externí odkaz:
http://arxiv.org/abs/2411.03618
Autor:
Yao, Zhen, Chuah, Mooi Choo
Recent video semantic segmentation (VSS) methods have demonstrated promising results in well-lit environments. However, their performance significantly drops in low-light scenarios due to limited visibility and reduced contextual details. In addition
Externí odkaz:
http://arxiv.org/abs/2411.00639
Disentangled representation learning (DRL) aims to break down observed data into core intrinsic factors for a profound understanding of the data. In real-world scenarios, manually defining and labeling these factors are non-trivial, making unsupervis
Externí odkaz:
http://arxiv.org/abs/2410.23820
Autor:
Fei, Fan, Choo, Jinhyun
The phase-field method has become popular for the numerical modeling of fluid-filled fractures, thanks to its ability to represent complex fracture geometry without algorithms. However, the algorithm-free representation of fracture geometry poses a s
Externí odkaz:
http://arxiv.org/abs/2410.20039
This paper explores integrating Automatic Speech Recognition (ASR) into natural language query systems to improve weather forecasting efficiency for Korean meteorologists. We address challenges in developing ASR systems for the Korean weather domain,
Externí odkaz:
http://arxiv.org/abs/2410.18444
Autor:
Yun, Jooyeol, Abati, Davide, Omran, Mohamed, Choo, Jaegul, Habibian, Amirhossein, Wiggers, Auke
Generative models have become a powerful tool for image editing tasks, including object insertion. However, these methods often lack spatial awareness, generating objects with unrealistic locations and scales, or unintentionally altering the scene ba
Externí odkaz:
http://arxiv.org/abs/2410.13564
Large language models (LLMs) serve as giant information stores, often including personal or copyrighted data, and retraining them from scratch is not a viable option. This has led to the development of various fast, approximate unlearning techniques
Externí odkaz:
http://arxiv.org/abs/2410.13274
Autor:
Oh, Alice, Noh, Inyoung, Choo, Jian, Lee, Jihoo, Park, Justin, Hwang, Kate, Kim, Sanghyeon, Oh, Soo Min
Brain tumor detection and classification are critical tasks in medical image analysis, particularly in early-stage diagnosis, where accurate and timely detection can significantly improve treatment outcomes. In this study, we apply various statistica
Externí odkaz:
http://arxiv.org/abs/2410.12692