Prompt-augmented temporal point process for streaming event sequence
Abstract Neural Temporal Point Processes (TPPs) are the prevalent paradigm for modeling
continuous-time event sequences, such as user activities on the web and financial …
continuous-time event sequences, such as user activities on the web and financial …
Towards anytime fine-tuning: Continually pre-trained language models with hypernetwork prompt
Continual pre-training has been urgent for adapting a pre-trained model to a multitude of
domains and tasks in the fast-evolving world. In practice, a continually pre-trained model is …
domains and tasks in the fast-evolving world. In practice, a continually pre-trained model is …
Deep optimal timing strategies for time series
C Pan, F Zhou, X Hu, X Zhu, W Ning, Z Zhuang… - arXiv preprint arXiv …, 2023 - arxiv.org
Deciding the best future execution time is a critical task in many business activities while
evolving time series forecasting, and optimal timing strategy provides such a solution, which …
evolving time series forecasting, and optimal timing strategy provides such a solution, which …
[HTML][HTML] Continual Learning for Time Series Forecasting: A First Survey
Q Besnard, N Ragot - Engineering Proceedings, 2024 - mdpi.com
Deep learning has brought significant advancements in the field of artificial intelligence,
particularly in robotics, imaging, sound processing, etc. However, a common major …
particularly in robotics, imaging, sound processing, etc. However, a common major …
Class-wise classifier design capable of continual learning using adaptive resonance theory-based topological clustering
This paper proposes a supervised classification algorithm capable of continual learning by
utilizing an Adaptive Resonance Theory (ART)-based growing self-organizing clustering …
utilizing an Adaptive Resonance Theory (ART)-based growing self-organizing clustering …