4 accepted papers
Large language models (LLMs) have recently achieved remarkable progress in sentence-level machine translation, but scaling to document-level machine translation (DocMT) remains challenging, particularly in modeling long-range dependencies and discourse phenomena across sentences and paragraphs. Docu
In reinforcement learning (RL), learning directly from pixels, is commonly known as vision-based RL. Effective state representations are crucial for high performance in vision-based RL. However, in order to learn effective state representations, most current vision-based RL methods based on contrast…
Recently, deep neural networks (DNNs) have achieved excellent performance on time series classification. However, DNNs require large amounts of labeled data for supervised training. Although data augmentation can alleviate this problem, the standard approach assigns the same label to all augmented s…
Time series clustering is an essential unsupervised technique in cases when category information is not available. It has been widely applied to genome data, anomaly detection, and in general, in any domain where pattern detection is important. Although feature-based time series clustering methods a…