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Anomaly detection (AD) is an important machine learning task with many real-world uses, including fraud detection, medical diagnosis, and industrial monitoring.
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Diederik P. Kingma and Max Welling. 2014 · 2014
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Charu C Aggarwal. 2015 · 2015
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Fraud detection system: A survey
Aisha Abdallah, Mohd Aizaini Maarof, and Anazida Zainal. 2016 · 2016
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The effects of varying class distribution on learner behavior for medicare fraud detection with imbalanced big data
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Bert: Pre-training of deep bidirectional transformers for language understanding
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Generative adversarial active learning for unsupervised outlier detection
Yezheng Liu, Zhe Li, Chong Zhou, Yuanchun Jiang, Jianshan Sun, Meng Wang, and Xiangnan He. 2019 · 2019
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Self-attentive, multi-context one-class classification for unsupervised anomaly detection on text
Lukas Ruff, Yury Zemlyanskiy, Robert Vandermeulen, Thomas Schnake, and Marius Kloft. 2019 · 2019
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Pyod: A python toolbox for scalable outlier detection
Yue Zhao, Zain Nasrullah, and Zheng Li. 2019 · 2019
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Deep learning for misinformation detection on online social networks: a survey and new perspectives
Md Rafiqul Islam, Shaowu Liu, Xianzhi Wang, and Guandong Xu. 2020 · 2020
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A survey of data augmentation approaches for NLP
Steven Y. Feng, Varun Gangal, Jason Wei, Sarath Chandar, Soroush Vosoughi, Teruko Mitamura, and Eduard Hovy. 2021 · 2021
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Deep learning for medical anomaly detection–a survey
Tharindu Fernando, Harshala Gammulle, Simon Denman, Sridha Sridharan, and Clinton Fookes. 2021 · 2021
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Date: Detecting anomalies in text via self-supervision of transformers
Andrei Manolache, Florin Brad, and Elena Burceanu. 2021 · 2021
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A review on social spam detection: Challenges, open issues, and future directions
Sanjeev Rao, Anil Kumar Verma, and Tarunpreet Bhatia. 2021 · 2021
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Automatic unsupervised outlier model selection
Yue Zhao, Ryan Rossi, and Leman Akoglu. 2021 · 2021
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Lunar: Unifying local outlier detection methods via graph neural networks
Adam Goodge, Bryan Hooi, See-Kiong Ng, and Wee Siong Ng. 2022 · 2022
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Adbench: Anomaly detection benchmark
Songqiao Han, Xiyang Hu, Hailiang Huang, Minqi Jiang, and Yue Zhao. 2022 · 2022
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Language models (mostly) know what they know
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Ecod: Unsupervised outlier detection using empirical cumulative distribution functions
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Visual classification via description from large language models
Sachit Menon and Carl Vondrick. 2022 · 2022
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On the importance of building high-quality training datasets for neural code search
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Chain-of-thought prompting elicits reasoning in large language models
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Amoeballm: Constructing any-shape large language models for efficient and instant deployment
Yonggan Fu, Zhongzhi Yu, Junwei Li, Jiayi Qian, Yongan Zhang, Xiangchi Yuan, Dachuan Shi, Roman Yakunin, and Yingyan Celine Lin. 2024 · 2024
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Insights into llm long-context failures: When transformers know but don’t tell
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Generative ai for synthetic data generation: Methods, challenges and the future
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Increasing diversity while maintaining accuracy: Text data generation with large language models and human interventions
John Chung, Ece Kamar, and Saleema Amershi. 2023 · 2023
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Qlora: efficient finetuning of quantized llms (2023)
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Specializing smaller language models towards multi-step reasoning
Yao Fu, Hao Peng, Litu Ou, Ashish Sabharwal, and Tushar Khot. 2023 · 2023
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Retrieval-augmented generation for large language models: A survey
Yunfan Gao, Yun Xiong, Xinyu Gao, Kangxiang Jia, Jinliu Pan, Yuxi Bi, Yi Dai, Jiawei Sun, and Haofen Wang. 2023 · 2023
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Biased-predicate annotation identification via unbiased visual predicate representation
Li Li, Chenwei Wang, You Qin, Wei Ji, and Renjie Liang. 2023 · 2023
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How good are LLMs at out-of-distribution detection?
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Metaood: Automatic selection of ood detection models
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Real-time anomaly detection and reactive planning with large language models
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A simple and effective pruning approach for large language models
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WizardLM: Empowering large pre-trained language models to follow complex instructions
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Large language models for anomaly and out-of-distribution detection: A survey
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Data augmentation is a hyperparameter: Cherry-picked self-supervision for unsupervised anomaly detection is creating the illusion of success
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Towards reproducible, automated, and scalable anomaly detection
Yue Zhao. 2024 · 2024
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Do llms understand visual anomalies? uncovering llm’s capabilities in zero-shot anomaly detection
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Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning
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Openai o3-mini
OpenAI. 2025 · 2025
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Efficient model selection for time series forecasting via llms
Wang Wei, Tiankai Yang, Hongjie Chen, Ryan A Rossi, Yue Zhao, Franck Dernoncourt, and Hoda Eldardiry. 2025 · 2025
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A survey of uncertainty estimation methods on large language models
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