A common assumption of novelty detection is that the distribution of both "normal" and "novel" data are static. This, however, is often not the case - for example scenarios where data evolves over time or scenarios in which the definition of normal and novel depends on contextual information, both leading to changes in these distributions. This can lead to significant difficulties when attempting to train a model on datasets where the distribution of normal data in one scenario is similar to that of novel data in another scenario. In this paper we propose a context-aware approach to novelty detection for deep autoencoders to address these difficulties. We create a semi-supervised network architecture that utilises auxiliary labels to reveal contextual information and allow the model to adapt to a variety of contexts in which the definitions of normal and novel change. We evaluate our approach on both image data and real world audio data displaying these characteristics and show that the performance of individually trained models can be achieved in a single model.
SPARKE: Scalable Prompt-Aware Diversity and Novelty Guidance in Diffusion Models via RKE Score
Department of Computer Science and Engineering, The Chinese University of Hong Kong（计算机科学与工程系，香港中文大学）；Department of Information Engineering, The Chinese University of Hong Kong（信息工程系，香港中文大学）
Deep Context-Aware Recommender System Utilizing Sequential Latent Context
Ben-Gurion University of the Negev（内盖夫本-古里安大学）；Telekom Innovation Laboratories at BGU（BGU德国电信创新实验室）；Stern School of Business, New York University（纽约大学斯特恩商学院）
Generative and multimodal AI for materials prediction and design: Progress, challenges, and perspectives
School of Computer Science, University of Sheffield, Sheffield, UK（计算机科学学院，谢菲尔德大学）；Centre for Machine Intelligence, University of Sheffield, Sheffield, UK（智能机器研究中心，谢菲尔德大学）；School of Chemical, Materials and Biological Engineering, University of Sheffield, Sheffield, UK（化学、材料与生物工程学院，谢菲尔德大学）；Henry Royce Institute, Royce Discovery Centre, University of Sheffield, Sheffield, UK（亨利·罗伊奇研究所，谢菲尔德大学）；Materials Nexus Ltd., Salisbury House, Cambridge, UK（材料 nexus 有限公司，剑桥，英国）；School of Mathematical and Physical Sciences, University of Sheffield, Sheffield, UK（数学与物理科学学院，谢菲尔德大学）
InceptionGCN: Receptive Field Aware Graph Convolutional Network for Disease Prediction
Technical University of Munich（慕尼黑工业大学）；Sharif University of Technology（谢里夫理工大学）；National Institute of Technology Tiruchirappalli（蒂鲁吉拉伯利国家理工学院）；Ludwig Maximilians Universität München（路德维希-马克西米利安慕尼黑大学）；Klinikum der Universität München（慕尼黑大学医院）；Johns Hopkins University（约翰斯·霍普金斯大学）
