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Titlebook: Machine Learning and Knowledge Discovery in Databases: Applied Data Science Track; European Conference, Yuxiao Dong,Dunja Mladeni?,Craig Sa

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樓主: Retina
51#
發(fā)表于 2025-3-30 08:44:53 | 只看該作者
52#
發(fā)表于 2025-3-30 13:37:53 | 只看該作者
ssential for reproducible results.Contains key notes and imp.The innate immune response is a crucial component of early resistance to infection, and it is now revealing increasing levels of complexity. The ability to modify the genome in vivo, has facilitated understanding of complex interactions be
53#
發(fā)表于 2025-3-30 17:51:08 | 只看該作者
Social Influence Attentive Neural Network for Friend-Enhanced RecommendationER) in this paper. In FER, a user is recommended with items liked/shared by his/her friends (called a friend referral circle). These friend referrals are explicitly shown to users. Different from conventional social recommendation, the unique friend referral circle in FER may significantly change th
54#
發(fā)表于 2025-3-30 21:19:08 | 只看該作者
Feedback-Guided Attributed Graph Embedding for Relevant Video Recommendationcommerce to computational biology. However, generating satisfactory video embeddings and putting them into practical use to improve the performance of recommendation tasks remains a challenge. In this paper, we present a video embedding approach named Equuleus, which learns video embeddings from use
55#
發(fā)表于 2025-3-31 04:11:20 | 只看該作者
56#
發(fā)表于 2025-3-31 07:49:38 | 只看該作者
Learning a Contextual and Topological Representation of Areas-of-Interest for On-Demand Delivery Appations learn either from sparse check-in histories or topological geometries, thus are either lacking coverage and violating the geographical law or ignoring contextual information from data. In this paper, we propose a novel representation learning framework for obtaining a unified representation o
57#
發(fā)表于 2025-3-31 09:30:13 | 只看該作者
58#
發(fā)表于 2025-3-31 16:34:39 | 只看該作者
59#
發(fā)表于 2025-3-31 21:30:20 | 只看該作者
RADAR: Recurrent Autoencoder Based Detector for Adversarial Examples on Temporal EHRal diagnosis and regulatory decisions. Although deep learning models have advantages over the traditional machine learning approaches in the medical domain, the discovery of adversarial examples has exposed great threats to the state-of-art deep learning medical systems. While most of the existing s
60#
發(fā)表于 2025-3-31 22:25:05 | 只看該作者
Self-supervised Log Parsing, which enables a variety of critical tasks such as troubleshooting and fault detection. However, large-scale software systems generate massive volumes of semi-structured log records, posing a major challenge for automated analysis. Parsing semi-structured records with free-form text log messages in
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