Titlebook: Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track; European Conference, Albert Bifet,Tomas Krilavičius,Slaw

Ancestor

Yao Liu,Yongfei Zhang,Xin Wangfective. Originating in Japan, lesson study has gained significant momentum in the mathematics education community in recent years.As a process for professional development, lesson study became highly visible when it was proposed as a means of supporting the common practice of promoting better teach

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Chameleon

PeersimGym: An Environment for Solving the Task Offloading Problem with Reinforcement Learninghallenges, including minimizing latency and energy usage under strict communication and storage constraints. While traditional optimization falls short in scalability; and heuristic approaches lack in achieving optimal outcomes, Reinforcement Learning (RL) offers a promising avenue by enabling the l

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揭穿真相

Self-SLAM: A Self-supervised Learning Based Annotation Method to Reduce Labeling Overheadse prediction, and surface classification. However, a major challenge in developing models for these tasks requires a large amount of labeled data for accurate predictions. The manual annotation process for a large dataset is expensive, time-consuming, and error-prone. Thus, we present SSLAM (Self-s

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Multi-intent Driven Contrastive Sequential Recommendationively mine the self-supervised signals to mitigate the data sparsity problem. However, current contrastive SR models overlook the intricate correlations among different users, leading to the false negative pair problem and adversely affecting recommendation performance. Therefore, in this paper, we