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Found 20 papers, 9 papers with code
DACAD: Domain Adaptation Contrastive Learning for Anomaly Detection in Multivariate Time Series
In this paper, we propose a novel Domain Adaptation Contrastive learning for Anomaly Detection in multivariate time series (DACAD) model to address this issue by combining UDA and contrastive representation learning.
Human Brain Exhibits Distinct Patterns When Listening to Fake Versus Real Audio: Preliminary Evidence
In this paper we study the variations in human brain activity when listening to real and fake audio.
EEG2Rep: Enhancing Self-supervised EEG Representation Through Informative Masked Inputs
We show that our semantic subsequence preserving improves the existing masking methods in self-prediction literature and find that preserving 50\% of EEG recordings will result in the most accurate results on all 6 tasks on average.
Guarding the Grid: Enhancing Resilience in Automated Residential Demand Response Against False Data Injection Attacks
Utility companies are increasingly leveraging residential demand flexibility and the proliferation of smart/IoT devices to enhance the effectiveness of residential demand response (DR) programs through automated device scheduling.
Series2Vec: Similarity-based Self-supervised Representation Learning for Time Series Classification
Our evaluation of Series2Vec on nine large real-world datasets, along with the UCR/UEA archive, shows enhanced performance compared to current state-of-the-art self-supervised techniques for time series.
Open-Set Graph Anomaly Detection via Normal Structure Regularisation
However, current methods tend to over-emphasise fitting the seen anomalies, leading to a weak generalisation ability to detect unseen anomalies, i. e., those that are not illustrated by the labelled anomaly nodes.
CARLA: Self-supervised Contrastive Representation Learning for Time Series Anomaly Detection
Our research shows the potential of contrastive representation learning to advance time series anomaly detection.
Improving Position Encoding of Transformers for Multivariate Time Series Classification
We then proposed a new absolute position encoding method dedicated to time series data called time Absolute Position Encoding (tAPE).
Ranked #1 on
Time Series Classification
on Heartbeat
Proximity Forest 2.0: A new effective and scalable similarity-based classifier for time series
Time series classification (TSC) is a challenging task due to the diversity of types of feature that may be relevant for different classification tasks, including trends, variance, frequency, magnitude, and various patterns.
Deep Learning for Time Series Classification and Extrinsic Regression: A Current Survey
Time Series Classification and Extrinsic Regression are important and challenging machine learning tasks.
Cross-Domain Graph Anomaly Detection via Anomaly-aware Contrastive Alignment
In doing so, ACT effectively transfers anomaly-informed knowledge from the source graph to learn the complex node relations of the normal class for GAD on the target graph without any specification of the anomaly distributions.
Deep Learning for Time Series Anomaly Detection: A Survey
Time series anomaly detection has applications in a wide range of research fields and applications, including manufacturing and healthcare.
Disjoint-CNN for Multivariate Time Series Classification
Time series classification algorithms have been mainly dominated by non-deep learning models.
Ranked #1 on
Time Series Classification
on pendigits
Detecting Singleton Spams in Reviews via Learning Deep Anomalous Temporal Aspect-Sentiment Patterns
Meanwhile, opinion spams spread widely and the detection of spam reviews becomes critically important for ensuring the integrity of the echo system of online reviews.
An Eager Splitting Strategy for Online Decision Trees
In this work, we study in ensemble settings the effectiveness of replacing the split strategy for the state-of-the-art online tree learner, Hoeffding Tree, with a rigorous but more eager splitting strategy that we had previously published as Hoeffding AnyTime Tree.
Emergent and Unspecified Behaviors in Streaming Decision Trees
Hoeffding trees are the state-of-the-art methods in decision tree learning for evolving data streams.
Detecting Driver's Distraction using Long-term Recurrent Convolutional Network
In this study we demonstrate a novel Brain Computer Interface (BCI) approach to detect driver distraction events to improve road safety.
Enabling Efficient Privacy-Assured Outlier Detection over Encrypted Incremental Datasets
It realises several cryptographic modules via efficient and interchangeable protocols to support the above cryptographic operations and composes them in the overall protocol to enable outlier detection over encrypted datasets.
Online Semi-Supervised Concept Drift Detection with Density Estimation
The two types of drift that are extensively studied are real drift and virtual drift where the former is the change in posterior probabilities p(y|X) while the latter is the change in distribution of X without affecting the posterior probabilities.
Extremely Fast Decision Tree
We introduce a novel incremental decision tree learning algorithm, Hoeffding Anytime Tree, that is statistically more efficient than the current state-of-the-art, Hoeffding Tree.
