U-STS-LLM uses a spatio-temporally steered LLM with dynamic attention bias generation to achieve state-of-the-art results on long-horizon traffic forecasting and high-missing-rate imputation while remaining parameter-efficient.
One fits all: Power general time series analysis by pretrained lm
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Proposes TA2CL framework that uses temporal asynchronous alignment in contrastive learning to improve cross-subject EEG emotion classification, reporting 64.5% accuracy on 9-class FACED, 79.5% binary on FACED, 86.4% on SEED and 70.1% on SEED-V.
KEMM-Net enriches power load time series representations with cross-modal text and visual knowledge via PID-guided contrastive learning to outperform baselines in few-shot forecasting scenarios.
RadarPLM adapts PLMs for marine radar target detection with lightweight adaptation and selective fine-tuning based on online learning values, reporting at least 6.35% average detection gains in low SCR conditions.
N-BEATS outperformed other models including LSTM and TFT in forecasting time to stability on sparse KATRIN tritium monitoring data.
citing papers explorer
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U-STS-LLM A Unified Spatio-Temporal Steered Large Language Model for Traffic Prediction and Imputation
U-STS-LLM uses a spatio-temporally steered LLM with dynamic attention bias generation to achieve state-of-the-art results on long-horizon traffic forecasting and high-missing-rate imputation while remaining parameter-efficient.
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Cross-Subject EEG Emotion Recognition Based on Temporal Asynchronous Alignment Contrastive Learning
Proposes TA2CL framework that uses temporal asynchronous alignment in contrastive learning to improve cross-subject EEG emotion classification, reporting 64.5% accuracy on 9-class FACED, 79.5% binary on FACED, 86.4% on SEED and 70.1% on SEED-V.
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Beyond Information Redundancy: Expanding Cross-Modal Knowledge Representation for Power Load Time Series Forecasting
KEMM-Net enriches power load time series representations with cross-modal text and visual knowledge via PID-guided contrastive learning to outperform baselines in few-shot forecasting scenarios.
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RadarPLM: Adapting Pre-trained Language Models for Marine Radar Target Detection by Selective Fine-tuning
RadarPLM adapts PLMs for marine radar target detection with lightweight adaptation and selective fine-tuning based on online learning values, reporting at least 6.35% average detection gains in low SCR conditions.
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Forecasting Source Stability in Scientific Experiments using Temporal Learning Models: A Case Study from Tritium Monitoring
N-BEATS outperformed other models including LSTM and TFT in forecasting time to stability on sparse KATRIN tritium monitoring data.