22 citations · 65 across the 11 of their papers we have counts for
12 papers
Anomaly Detection of Tabular Data Using LLMs
Aodong Li, Yunhan Zhao, Chen Qiu +4
Large language models (LLMs) have shown their potential in long-context understanding and mathematical reasoning. In this paper, we study the problem of using LLMs to detect tabula…
Uncertainty-aware Evaluation of Auxiliary Anomalies with the Expected Anomaly Posterior
Lorenzo Perini, Maja Rudolph, Sabrina Schmedding +1
Anomaly detection is the task of identifying examples that do not behave as expected. Because anomalies are rare and unexpected events, collecting real anomalous examples is often…
Model Selection of Anomaly Detectors in the Absence of Labeled Validation Data
Clement Fung, Chen Qiu, Aodong Li +1
Anomaly detection is the task of identifying abnormal samples in large unlabeled datasets. While the advent of foundation models has produced powerful zero-shot anomaly detection m…
Galaxy Light profile neural Networks (GaLNets). II. Bulge-Disc decomposition in optical space-based observations
Chen Qiu, Nicola R. Napolitano, Rui Li +12
Bulge-disk (B-D) decomposition is an effective diagnostic to characterize the galaxy morphology and understand its evolution across time. So far, high-quality data have allowed det…
Zero-Shot Anomaly Detection via Batch Normalization
Aodong Li, Chen Qiu, Marius Kloft +3
Anomaly detection (AD) plays a crucial role in many safety-critical application domains. The challenge of adapting an anomaly detector to drift in the normal data distribution, esp…
Deep Anomaly Detection under Labeling Budget Constraints
Aodong Li, Chen Qiu, Marius Kloft +3
Selecting informative data points for expert feedback can significantly improve the performance of anomaly detection (AD) in various contexts, such as medical diagnostics or fraud…
Raising the Bar in Graph-level Anomaly Detection
Chen Qiu, Marius Kloft, Stephan Mandt +1
Graph-level anomaly detection has become a critical topic in diverse areas, such as financial fraud detection and detecting anomalous activities in social networks. While most rese…
Detecting Anomalies within Time Series using Local Neural Transformations
Tim Schneider, Chen Qiu, Marius Kloft +4
We develop a new method to detect anomalies within time series, which is essential in many application domains, reaching from self-driving cars, finance, and marketing to medical d…
Latent Outlier Exposure for Anomaly Detection with Contaminated Data
Chen Qiu, Aodong Li, Marius Kloft +2
Anomaly detection aims at identifying data points that show systematic deviations from the majority of data in an unlabeled dataset. A common assumption is that clean training data…
Neural Transformation Learning for Deep Anomaly Detection Beyond Images
Chen Qiu, Timo Pfrommer, Marius Kloft +2
Data transformations (e.g. rotations, reflections, and cropping) play an important role in self-supervised learning. Typically, images are transformed into different views, and neu…
Variational Dynamic Mixtures
Chen Qiu, Stephan Mandt, Maja Rudolph
Deep probabilistic time series forecasting models have become an integral part of machine learning. While several powerful generative models have been proposed, we provide evidence…
Learning Topometric Semantic Maps from Occupancy Grids
Markus Hiller, Chen Qiu, Florian Particke +2
Today's mobile robots are expected to operate in complex environments they share with humans. To allow intuitive human-robot collaboration, robots require a human-like understandin…