Results 11 to 20 of about 343,541 (352)
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
semanticscholar +1 more source
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
semanticscholar +1 more source
Outlier Weighed Layerwise Sparsity (OWL): A Missing Secret Sauce for Pruning LLMs to High Sparsity [PDF]
International Conference on Machine Learning, 2023 Large Language Models (LLMs), renowned for their remarkable performance across diverse domains, present a challenge when it comes to practical deployment due to their colossal model size.
Lu Yin +9 more
semanticscholar +1 more source
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
semanticscholar +1 more source
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
semanticscholar +1 more source
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
semanticscholar +1 more source
, 2015 We introduce a generalization of differential privacy called tailored differential privacy, where an individual’s privacy parameter is “tailored ” for the individual based on the individual’s data and the data set.
Edward Lui, Rafael Pass
openaire +4 more sources
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
semanticscholar +1 more source
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
semanticscholar +1 more source
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
semanticscholar +1 more source