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Found 30 papers, 8 papers with code
Interpretable Data-driven Anomaly Detection in Industrial Processes with ExIFFI
Anomaly detection (AD) is a crucial process often required in industrial settings.
Enabling Efficient and Flexible Interpretability of Data-driven Anomaly Detection in Industrial Processes with AcME-AD
While Machine Learning has become crucial for Industry 4. 0, its opaque nature hinders trust and impedes the transformation of valuable insights into actionable decision, a challenge exacerbated in the evolving Industry 5. 0 with its human-centric focus.
Multi-Label Continual Learning for the Medical Domain: A Novel Benchmark
This method aims to mitigate forgetting while adapting to new classes and domain shifts by combining the advantages of the Replay and Pseudo-Label methods and solving their limitations in the proposed scenario.
A Fairness-Oriented Reinforcement Learning Approach for the Operation and Control of Shared Micromobility Services
As Machine Learning systems become increasingly popular across diverse application domains, including those with direct human implications, the imperative of equity and algorithmic fairness has risen to prominence in the Artificial Intelligence community.
Unveiling the Anomalies in an Ever-Changing World: A Benchmark for Pixel-Level Anomaly Detection in Continual Learning
Anomaly Detection is a relevant problem in numerous real-world applications, especially when dealing with images.
AcME-AD: Accelerated Model Explanations for Anomaly Detection
Pursuing fast and robust interpretability in Anomaly Detection is crucial, especially due to its significance in practical applications.
Exploiting Estimation Bias in Deep Double Q-Learning for Actor-Critic Methods
This paper introduces innovative methods in Reinforcement Learning (RL), focusing on addressing and exploiting estimation biases in Actor-Critic methods for continuous control tasks, using Deep Double Q-Learning.
Bayesian Deep Learning for Remaining Useful Life Estimation via Stein Variational Gradient Descent
In particular, we show through experimental studies on simulated run-to-failure turbofan engine degradation data that Bayesian deep learning models trained via Stein variational gradient descent consistently outperform with respect to convergence speed and predictive performance both the same models trained via parametric variational inference and their frequentist counterparts trained via backpropagation.
ExIFFI and EIF+: Interpretability and Enhanced Generalizability to Extend the Extended Isolation Forest
The analysis demonstrates the effectiveness of ExIFFI in providing explanations for AD predictions.
Heterogeneous Domain Adaptation and Equipment Matching: DANN-based Alignment with Cyclic Supervision (DBACS)
Motivated by the need to transfer and standardize established processes to different, non-identical environments and by the challenge of adapting to heterogeneous data representations, this work introduces the Domain Adaptation Neural Network with Cyclic Supervision (DBACS) approach.
Time series Forecasting to detect anomalous behaviours in Multiphase Flow Meters
An Anomaly Detection (AD) System for Self-diagnosis has been developed for Multiphase Flow Meter (MPFM).
Continual Learning Approaches for Anomaly Detection
The proposed technique scales and compresses the original images using a Super Resolution model which, to the best of our knowledge, is studied for the first time in the Continual Learning setting.
Band Relevance Factor (BRF): a novel automatic frequency band selection method based on vibration analysis for rotating machinery
The monitoring of rotating machinery has now become a fundamental activity in the industry, given the high criticality in production processes.
Fault Diagnosis using eXplainable AI: a Transfer Learning-based Approach for Rotating Machinery exploiting Augmented Synthetic Data
Artificial Intelligence (AI) is one of the approaches that has been proposed to analyze the collected data (e. g., vibration signals) providing a diagnosis of the asset's operating condition.
A Multi-label Continual Learning Framework to Scale Deep Learning Approaches for Packaging Equipment Monitoring
For the first time, we study multi-label classification in the Domain Incremental Learning scenario.
Active Learning-based Isolation Forest (ALIF): Enhancing Anomaly Detection in Decision Support Systems
The detection of anomalous behaviours is an emerging need in many applications, particularly in contexts where security and reliability are critical aspects.
On the Properties of Adversarially-Trained CNNs
Adversarial Training has proved to be an effective training paradigm to enforce robustness against adversarial examples in modern neural network architectures.
Learning to Rank from Relevance Judgments Distributions
Learning to Rank (LETOR) algorithms are usually trained on annotated corpora where a single relevance label is assigned to each available document-topic pair.
AcME -- Accelerated Model-agnostic Explanations: Fast Whitening of the Machine-Learning Black Box
In the context of human-in-the-loop Machine Learning applications, like Decision Support Systems, interpretability approaches should provide actionable insights without making the users wait.
Improving Robustness with Image Filtering
By leveraging the IGE representation, we build a new defense method, Filtering As a Defense, that does not allow the attacker to entangle pixels to create malicious patterns.
TiWS-iForest: Isolation Forest in Weakly Supervised and Tiny ML scenarios
Unsupervised anomaly detection tackles the problem of finding anomalies inside datasets without the labels availability; since data tagging is typically hard or expensive to obtain, such approaches have seen huge applicability in recent years.
Lazy FSCA for Unsupervised Variable Selection
In this paper a "lazy" implementation of the FSCA algorithm (L-FSCA) is proposed, which, although not equivalent to FSCA due to the absence of submodularity, has the potential to yield comparable performance while being up to an order of magnitude faster to compute.
An Explainable Artificial Intelligence Approach for Unsupervised Fault Detection and Diagnosis in Rotating Machinery
Therefore, a new approach for fault detection and diagnosis in rotating machinery is here proposed.
Neural Feature Selection for Learning to Rank
LEarning TO Rank (LETOR) is a research area in the field of Information Retrieval (IR) where machine learning models are employed to rank a set of items.
$β$-Variational Classifiers Under Attack
Deep Neural networks have gained lots of attention in recent years thanks to the breakthroughs obtained in the field of Computer Vision.
IntroVAC: Introspective Variational Classifiers for Learning Interpretable Latent Subspaces
Learning useful representations of complex data has been the subject of extensive research for many years.
Proximal Deterministic Policy Gradient
This paper introduces two simple techniques to improve off-policy Reinforcement Learning (RL) algorithms.
Adversarial Training Reduces Information and Improves Transferability
Recent results show that features of adversarially trained networks for classification, in addition to being robust, enable desirable properties such as invertibility.
Interpretable Anomaly Detection with DIFFI: Depth-based Isolation Forest Feature Importance
Anomaly Detection is an unsupervised learning task aimed at detecting anomalous behaviours with respect to historical data.
Directional Adversarial Training for Cost Sensitive Deep Learning Classification Applications
Adversarial Training is a training procedure aiming at providing models that are robust to worst-case perturbations around predefined points.
