On when is Reservoir Computing with Cellular Automata Beneficial?
원문 초록
Reservoir Computing with Cellular Automata (ReCA) is a relatively novel and promising approach. It consists of 3 steps: an encoding scheme to inject the problem into the CA, the CA iterations step itself and a simple classifying step, typically a linear classifier. This paper demonstrates that the ReCA concept is effective even in arguably the simplest implementation of a ReCA system. However, we also report a failed attempt on the UCR Time Series Classification Archive where ReCA seems to work, but only because of the encoding scheme itself, not in any part due to the CA. This highlights the need for ablation testing, i.e., comparing internally with sub-parts of one model, but also raises an open question on what kind of tasks ReCA is best suited for.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
TimeMaster: Training Time-Series Multimodal LLMs to Reason via Reinforcement Learning
FreRA: A Frequency-Refined Augmentation for Contrastive Learning on Time Series Classification
Implet: A Post-hoc Subsequence Explainer for Time Series Models
Learning Soft Sparse Shapes for Efficient Time-Series Classification
PYRREGULAR: A Unified Framework for Irregular Time Series, with Classification Benchmarks
Lightweight Defense Against Adversarial Attacks in Time Series Classification
토론
이 논문과 연결된 포럼 글이 아직 없습니다.