arXiv AI
6d ago
Representation Finetuning for Continual Learning
arXiv:2603. 11201v3 Announce Type: replace-cross Abstract: The world is inherently dynamic, and continual learning aims to enable models to adapt to ever-evolving data streams.
Combining Statistical Features and Deep Encodings for Rehearsal-Based Class-Incremental Time Series Classification
arXiv:2606. 03292v1 Announce Type: cross Abstract: Many systems used in real-world environments require adding new categories and incorporating new information without forgetting what was previously learnt by the classification model.
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arXiv Machine Learning
Jun 11
CaReTS: A Multi-Task Framework Unifying Classification and Regression for Time Series Forecasting
arXiv:2511. 09789v2 Announce Type: replace Abstract: Recent advances in deep forecasting models have achieved remarkable performance, yet most approaches still struggle to provide both accurate predictions and interpretable insights into temporal dynamics.
arXiv Machine Learning
Jul 22
In-Context Time Series Classification with Random Convolutional Features
arXiv:2607. 19234v1 Announce Type: new Abstract: Time series classification is central to domains like medical signal analysis, industrial monitoring, and sensor-based activity recognition, where class information manifests as localized shapes, specific frequencies, temporal shifts, or complex cross-channel interactions.
arXiv AI
Jul 21
TypiCore: A Hybrid Active Query Strategy for Class-Incremental Learning on Time Series
arXiv:2607. 17632v1 Announce Type: cross Abstract: Time series data play a pivotal role across numerous domains, including healthcare and manufacturing.
arXiv AI
Jun 26
Data-Free Reservoir Features for Efficient Long-Horizon Cold-Start Continual Learning
arXiv:2606. 27095v1 Announce Type: cross Abstract: Cold-start exemplar-free class-incremental learning requires learning a growing set of classes without replay, external pretraining, or a large initial task.
arXiv Machine Learning
Aug 6
TS2TabPFN: Time Series Classification and Extrinsic Regression through Feature Extraction and a Tabular Foundation Model
arXiv:2608. 04174v1 Announce Type: new Abstract: Time series data are ubiquitous in practical applications, where classification (TSC) and extrinsic regression (TSER) have emerged as essential tasks for obtaining value from temporal sequences.
arXiv Machine Learning
1d ago
FETERS: Few-Shot Early Time-Series Classification via Effective Ratio Selection
arXiv:2608. 16385v1 Announce Type: new Abstract: Early time-series classification (ETSC) aims to make accurate predictions from partially observed time series as early as possible.
arXiv AI
Aug 11
FreSH: Frequency-Segmented Hierarchical Multi-Expert Framework for Multivariate Time Series Classification
arXiv:2608. 08207v1 Announce Type: cross Abstract: Multivariate Time Series Classification (MTSC) demands models that can effectively capture complex temporal patterns across multiple scales while remaining computationally efficient.
arXiv Machine Learning
Jul 10
Are Current Continual Learning Methods Truly Agnostic? Introducing OPRE, a Step Toward Agnostic Continual Learning
arXiv:2511. 08226v2 Announce Type: replace Abstract: In order to achieve Continual Learning (CL), the problem of catastrophic forgetting, one that has plagued neural networks since their inception, must be overcome.
arXiv AI
Jun 11
Time-Series Foundation Model Embeddings for Remaining Useful Life Estimation
arXiv:2606. 11990v1 Announce Type: cross Abstract: Remaining Useful Life (RUL) prediction is essential for industrial predictive maintenance, yet many learning-based approaches rely on extensive feature engineering or large labeled datasets to train task-specific sequence models.
Hugging Face Trending Papers
Aug 4
TS2TabPFN: Time Series Classification and Extrinsic Regression through Feature Extraction and a Tabular Foundation Model
Time series data are ubiquitous in practical applications, where classification (TSC) and extrinsic regression (TSER) have emerged as essential tasks for obtaining value from temporal sequences. While the literature has seen significant progress through feature-based and deep learning models, existing methods often focus either on the quality of feature extraction or on the intrinsic predictive power of complex architectures applied to raw data.
arXiv AI
Jul 24
Attention-based Experience Replay Framework for Continual Learning of Agnostic Time Series Forecasting Models
arXiv:2607. 20493v1 Announce Type: new Abstract: Deep learning has led to remarkable progress in artificial intelligence, particularly in robotics, imaging and sound processing.