5 papers
Source-Free Cross-Domain Continual Learning
Muhammad Tanzil Furqon, Mahardhika Pratama, Igor Å krjanc +3
Although existing cross-domain continual learning approaches successfully address many streaming tasks having domain shifts, they call for a fully labeled source domain hindering t…
A Neuro-Fuzzy System for Interpretable Long-Term Stock Market Forecasting
Miha Ožbot, Igor Škrjanc, Vitomir Štruc
In the complex landscape of multivariate time series forecasting, achieving both accuracy and interpretability remains a significant challenge. This paper introduces the Fuzzy Tran…
Black-Box Time-Series Domain Adaptation via Cross-Prompt Foundation Models
M. T. Furqon, Mahardhika Pratama, Igor Skrjanc +3
The black-box domain adaptation (BBDA) topic is developed to address the privacy and security issues where only an application programming interface (API) of the source model is av…
From Model-Based and Adaptive Control to Evolving Fuzzy Control
Daniel Leite, Igor Å krjanc, Fernando Gomide
Evolving fuzzy systems build and adapt fuzzy models - such as predictors and controllers - by incrementally updating their rule-base structure from data streams. On the occasion of…
Latest Advancements Towards Catastrophic Forgetting under Data Scarcity: A Comprehensive Survey on Few-Shot Class Incremental Learning
M. Anwar Ma'sum, Mahardhika Pratama, Igor Skrjanc
Data scarcity significantly complicates the continual learning problem, i.e., how a deep neural network learns in dynamic environments with very few samples. However, the latest pr…