Aug 10, 2024 · To address this, we propose using pre- trained language models for time series anomaly detection, leveraging their strong generalization capabilities.
Aug 8, 2024 · Abstract. Self-supervised methods have gained prominence in time series anomaly detection due to the scarcity of available annotations.
Missing: bert: bert
7 days ago · The initial network layers of the pre-trained model (the feature extraction networks) are frozen, whereas the lower layers (decision networks) are fine-tuned.
7 days ago · By pretraining on large-scale data, they have shown remarkable few-shot and even zero-shot performance across various downstream tasks. This has motivated an ...
Aug 26, 2024 · This paper presents an extensive data contamination report for over 15 popular large language models across six popular multiple-choice QA benchmarks.
Aug 22, 2024 · It introduces the novel “expand” mechanism in training sparse neural networks, allowing the model to dynamically shrink, expand, or remain stable to find a ...
5 days ago · ... via Masked Image Modeling Pre-Training ... A Study on Unsupervised Anomaly Detection and Defect Localization using Generative Model in Ultrasonic Non-Destructive ...
Aug 20, 2024 · Financial time series modeling is crucial for understanding and predicting market behaviors but faces challenges such as non-linearity, non-stationarity, ...
Missing: bert: bert
Aug 11, 2024 · In this paper, a new intrusion detection model is presented which uses a genetic algorithm (GA) for feature selection and optimization algorithms for gradient ...
Aug 16, 2024 · BERT and T5's achievement has spurred the study of the transformation of self-supervised objectives. Alternatives to masked language modeling include random ...