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Found 41 papers, 14 papers with code
Mixup Domain Adaptations for Dynamic Remaining Useful Life Predictions
MDAN encompasses a three-staged mechanism where the mix-up strategy is not only performed to regularize the source and target domains but also applied to establish an intermediate mix-up domain where the source and target domains are aligned.
Towards Cross-Domain Continual Learning
In this work, we introduce a novel approach called Cross-Domain Continual Learning (CDCL) that addresses the limitations of being limited to single supervised domains.
Dynamic Long-Term Time-Series Forecasting via Meta Transformer Networks
A slow learner tailors suitable representations to fast learners.
Cross-Domain Few-Shot Learning via Adaptive Transformer Networks
Most few-shot learning works rely on the same domain assumption between the base and the target tasks, hindering their practical applications.
Few-Shot Continual Learning via Flat-to-Wide Approaches
This paper proposes a few-shot continual learning approach, termed FLat-tO-WidE AppRoach (FLOWER), where a flat-to-wide learning process finding the flat-wide minima is proposed to address the catastrophic forgetting problem.
Enhancing Wind Power Forecast Precision via Multi-head Attention Transformer: An Investigation on Single-step and Multi-step Forecasting
The proposed model is evaluated for single-step and multi-step WPF, and compared with gated recurrent unit (GRU) and long short-term memory (LSTM) models on a wind power dataset.
Assessor-Guided Learning for Continual Environments
This paper proposes an assessor-guided learning strategy for continual learning where an assessor guides the learning process of a base learner by controlling the direction and pace of the learning process thus allowing an efficient learning of new environments while protecting against the catastrophic interference problem.
Class-Incremental Learning via Knowledge Amalgamation
Catastrophic forgetting has been a significant problem hindering the deployment of deep learning algorithms in the continual learning setting.
Scalable Adversarial Online Continual Learning
Adversarial continual learning is effective for continual learning problems because of the presence of feature alignment process generating task-invariant features having low susceptibility to the catastrophic forgetting problem.
Autonomous Cross Domain Adaptation under Extreme Label Scarcity
This paper aims to attack the problem of extreme label shortage in the cross domain multistream classification problems where only very few labelled samples of the source stream are provided before process runs.
Latent Preserving Generative Adversarial Network for Imbalance classification
Many real-world classification problems have imbalanced frequency of class labels; a well-known issue known as the "class imbalance" problem.
Reinforced Continual Learning for Graphs
Continual learning on graphs is largely unexplored and existing graph continual learning approaches are limited to the task-incremental learning scenarios.
Unsupervised Learning for Identifying High Eigenvector Centrality Nodes: A Graph Neural Network Approach
So, it is of the essence to develop a method that is scalable in low computational time.
ACDC: Online Unsupervised Cross-Domain Adaptation
We consider the problem of online unsupervised cross-domain adaptation, where two independent but related data streams with different feature spaces -- a fully labeled source stream and an unlabeled target stream -- are learned together.
Unsupervised Continual Learning in Streaming Environments
A deep clustering network is desired for data streams because of its aptitude in extracting natural features thus bypassing the laborious feature engineering step.
Automatic Online Multi-Source Domain Adaptation
Knowledge transfer across several streaming processes remain challenging problem not only because of different distributions of each stream but also because of rapidly changing and never-ending environments of data streams.
Unsupervised Continual Learning via Self-Adaptive Deep Clustering Approach
Unsupervised continual learning remains a relatively uncharted territory in the existing literature because the vast majority of existing works call for unlimited access of ground truth incurring expensive labelling cost.
Autonomous Deep Quality Monitoring in Streaming Environments
The common practice of quality monitoring in industry relies on manual inspection well-known to be slow, error-prone and operator-dependent.
Continual Learning via Inter-Task Synaptic Mapping
Learning from streaming tasks leads a model to catastrophically erase unique experiences it absorbs from previous episodes.
Scalable Teacher Forcing Network for Semi-Supervised Large Scale Data Streams
The large-scale data stream problem refers to high-speed information flow which cannot be processed in scalable manner under a traditional computing platform.
Weakly Supervised Deep Learning Approach in Streaming Environments
The feasibility of existing data stream algorithms is often hindered by the weakly supervised condition of data streams.
ATL: Autonomous Knowledge Transfer from Many Streaming Processes
It automatically evolves its network structure from scratch with/without the presence of ground truth to overcome independent concept drifts in the source and target domain.
DEVDAN: Deep Evolving Denoising Autoencoder
The Denoising Autoencoder (DAE) enhances the flexibility of the data stream method in exploiting unlabeled samples.
Automatic Construction of Multi-layer Perceptron Network from Streaming Examples
The advantage of NADINE, namely elastic structure and online learning trait, is numerically validated using nine data stream classification and regression problems where it demonstrates performance improvement over prominent algorithms in all problems.
Real-time UAV Complex Missions Leveraging Self-Adaptive Controller with Elastic Structure
At the same time, UAVs may need to operate under external disturbances to follow time-based trajectories.
Autonomous Deep Learning: Continual Learning Approach for Dynamic Environments
The feasibility of deep neural networks (DNNs) to address data stream problems still requires intensive study because of the static and offline nature of conventional deep learning approaches.
Autonomous Deep Learning: Incremental Learning of Denoising Autoencoder for Evolving Data Streams
The generative learning phase of Autoencoder (AE) and its successor Denosing Autoencoder (DAE) enhances the flexibility of data stream method in exploiting unlabelled samples.
An Incremental Construction of Deep Neuro Fuzzy System for Continual Learning of Non-stationary Data Streams
DEVFNN is developed under the stacked generalization principle via the feature augmentation concept where a recently developed algorithm, namely gClass, drives the hidden layer.
Deep Stacked Stochastic Configuration Networks for Lifelong Learning of Non-Stationary Data Streams
The concept of SCN offers a fast framework with universal approximation guarantee for lifelong learning of non-stationary data streams.
Evolving Large-Scale Data Stream Analytics based on Scalable PANFIS
Extensive experiments on this framework are validated by measuring the accuracy and running time of four combinations of Scalable PANFIS and other Spark-based built in algorithms.
An Online RFID Localization in the Manufacturing Shopfloor
{Radio Frequency Identification technology has gained popularity for cheap and easy deployment.
PALM: An Incremental Construction of Hyperplanes for Data Stream Regression
Data stream has been the underlying challenge in the age of big data because it calls for real-time data processing with the absence of a retraining process and/or an iterative learning approach.
Parsimonious Network based on Fuzzy Inference System (PANFIS) for Time Series Feature Prediction of Low Speed Slew Bearing Prognosis
From the results, it is suggested that PANFIS offers outstanding performance compared to those of other methods.
Online Tool Condition Monitoring Based on Parsimonious Ensemble+
The paper presents advancement of a newly developed ensemble learning algorithm, pENsemble+, where online active learning scenario is incorporated to reduce operator labelling effort.
An Automatic Contextual Analysis and Clustering Classifiers Ensemble approach to Sentiment Analysis
The base classifier of the ensemble method is a modified k-means algorithm.
Evolving Ensemble Fuzzy Classifier
pENsemble adopts a dynamic ensemble structure to output a final classification decision where it features a novel drift detection scenario to grow the ensemble structure.
PANFIS++: A Generalized Approach to Evolving Learning
We note at least three uncharted territories of existing EISs: data uncertainty, temporal system dynamic, redundant data streams.
Metacognitive Learning Approach for Online Tool Condition Monitoring
The learning process consists of three phases: what to learn, how to learn, when to learn and makes use of a generalized recurrent network structure as a cognitive component.
Parsimonious Random Vector Functional Link Network for Data Streams
The theory of random vector functional link network (RVFLN) has provided a breakthrough in the design of neural networks (NNs) since it conveys solid theoretical justification of randomized learning.
A novel online multi-label classifier for high-speed streaming data applications
In this paper, a high-speed online neural network classifier based on extreme learning machines for multi-label classification is proposed.
A Novel Online Real-time Classifier for Multi-label Data Streams
In this paper, a novel extreme learning machine based online multi-label classifier for real-time data streams is proposed.
