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EmDT: Embedding Diffusion Transformer for Tabular Data Generation in Fraud Detection

Kuo, En-Ya, Motsch, Sebastien

arXiv.org Machine Learning

Imbalanced datasets pose a difficulty in fraud detection, as classifiers are often biased toward the majority class and perform poorly on rare fraudulent transactions. Synthetic data generation is therefore commonly used to mitigate this problem. In this work, we propose the Clustered Embedding Diffusion-Transformer (EmDT), a diffusion model designed to generate fraudulent samples. Our key innovation is to leverage UMAP clustering to identify distinct fraudulent patterns, and train a Transformer denoising network with sinusoidal positional embeddings to capture feature relationships throughout the diffusion process. Once the synthetic data has been generated, we employ a standard decision-tree-based classifier (e.g., XGBoost) for classification, as this type of model remains better suited to tabular datasets. Experiments on a credit card fraud detection dataset demonstrate that EmDT significantly improves downstream classification performance compared to existing oversampling and generative methods, while maintaining comparable privacy protection and preserving feature correlations present in the original data.




Assembly Fuzzy Representation on Hypergraph for Open-Set 3D Object Retrieval Y ang Xu

Neural Information Processing Systems

The lack of object-level labels presents a significant challenge for 3D object retrieval in the open-set environment. However, part-level shapes of objects often share commonalities across categories but remain underexploited in existing retrieval methods. In this paper, we introduce the Hypergraph-Based Assembly Fuzzy Representation (HAFR) framework, which navigates the intricacies of open-set 3D object retrieval through a bottom-up lens of Part Assembly .


Welcome to the dark side of crypto's permissionless dream

MIT Technology Review

Jean-Paul Thorbjornsen is a leader of THORChain, a blockchain that is not supposed to have any leaders--and is reeling from a series of expensive controversies. We can do whatever we want," Jean-Paul Thorbjornsen tells me from the pilot's seat of his Aston Martin helicopter. As we fly over suburbs outside Melbourne, Australia, it's becoming clear that doing whatever he wants is Thorbjornsen's MO. Upper-middle-class homes give way to vineyards, and Thorbjornsen points out our landing spot outside a winery. "They're going to ask for a shot now," he says, used to the attention drawn by his luxury helicopter, emblazoned with the tail letters "BTC" for bitcoin (the price tag of $5 million in Australian dollars--$3.5 million in US dollars today--was perhaps reasonable for someone who claims a previous crypto project made more than AU$400 million, although he also says those funds were tied up in the company). Thorbjornsen is a founder of THORChain, a blockchain through which users can swap ...



NeuralGF: Unsupervised Point Normal Estimation by Learning Neural Gradient Function Qing Li

Neural Information Processing Systems

Normal estimation for 3D point clouds is a fundamental task in 3D geometry processing. The state-of-the-art methods rely on priors of fitting local surfaces learned from normal supervision.