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What Is the Outlier—Consistent Outlier or Inconsistent Outlier? [PDF]
Analytical Science AdvancesIn the design of molecules, materials and processes, outliers or outlier samples can be included in a dataset when performing machine learning or regression analysis.
Hiromasa Kaneko
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ECOD: Unsupervised Outlier Detection Using Empirical Cumulative Distribution Functions [PDF]
IEEE Transactions on Knowledge and Data Engineering, 2022 Outlier detection refers to the identification of data points that deviate from a general data distribution. Existing unsupervised approaches often suffer from high computational cost, complex hyperparameter tuning, and limited interpretability ...
Zheng Li +5 more
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OliVe: Accelerating Large Language Models via Hardware-friendly Outlier-Victim Pair Quantization [PDF]
International Symposium on Computer Architecture, 2023 Transformer-based large language models (LLMs) have achieved great success with the growing model size. LLMs' size grows by 240× every two years, which outpaces the hardware progress and makes model inference increasingly costly.
Cong Guo +8 more
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Non-Parametric Outlier Synthesis [PDF]
International Conference on Learning Representations, 2023 Out-of-distribution (OOD) detection is indispensable for safely deploying machine learning models in the wild. One of the key challenges is that models lack supervision signals from unknown data, and as a result, can produce overconfident predictions on ...
Leitian Tao +3 more
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Dream the Impossible: Outlier Imagination with Diffusion Models [PDF]
Neural Information Processing Systems, 2023 Utilizing auxiliary outlier datasets to regularize the machine learning model has demonstrated promise for out-of-distribution (OOD) detection and safe prediction.
Xuefeng Du +3 more
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Outlier Suppression: Pushing the Limit of Low-bit Transformer Language Models [PDF]
Neural Information Processing Systems, 2022 Transformer architecture has become the fundamental element of the widespread natural language processing~(NLP) models. With the trends of large NLP models, the increasing memory and computation costs hinder their efficient deployment on resource-limited
Xiuying Wei +7 more
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A Review on Outlier/Anomaly Detection in Time Series Data [PDF]
ACM Computing Surveys, 2020 Recent advances in technology have brought major breakthroughs in data collection, enabling a large amount of data to be gathered over time and thus generating time series.
Ane Blázquez-García +3 more
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Barekeng, 2022 The study aimed to compare a few robust approaches in linear regression in the presence of outlier and high leverage points. Ordinary least square (OLS) estimation of parameters is the most basic approach practiced widely in regression analysis. However,
Anwar Fitrianto, Sim Hui Xin
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Outlier Detection in High Dimensional Data
Regular Issue, 2021 Artificial intelligence (AI) is the science that allows computers to replicate human intelligence in areas such as decision-making, text processing, visual perception.
C. Aggarwal, Philip S. Yu
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COPOD: Copula-Based Outlier Detection [PDF]
Industrial Conference on Data Mining, 2020 Outlier detection refers to the identification of rare items that are deviant from the general data distribution. Existing approaches suffer from high computational complexity, low predictive capability, and limited interpretability.
Zheng Li +4 more
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