Energy
Task-oriented Time Series Imputation Evaluation via Generalized Representers Zhixian Wang 1,2, Linxiao Y ang
Specifically, we summarize our contributions into the following points.
Introducing Spectral Attention for Long-Range Dependency in Time Series Forecasting
Spectral Attention preserves long-period trends through a low-pass filter and facilitates gradient to flow between samples. Spectral Attention can be seamlessly integrated into most sequence models, allowing models with fixed-sized look-back windows to capture long-range dependencies over thousands of steps.
ChronoEpilogi: Scalable Time-Series Variable Selection with Multiple Solutions
Lasso performance, with the added benefit of providing multiple solutions.
A Practitioner's Guide to Continual Multimodal Pretraining
However, practical model deployment often operates in the gap between these two limit cases, as real-world applications demand adaptation to specific subdomains, tasks or concepts -- spread over the entire, varying life cycle of a model.
WFCRL: A Multi-Agent Reinforcement Learning Benchmark for Wind Farm Control
WFCRL also offers turbine load observations that will allow to optimize the farm performance while limiting turbine structural damages.
FUSU: A Multi-temporal-source Land Use Change Segmentation Dataset for Fine-grained Urban Semantic Understanding
Fine urban change segmentation using multi-temporal remote sensing images is essential for understanding human-environment interactions in urban areas.
Geometry-aware training of factorized layers in tensor Tucker format
Reducing parameter redundancies in neural network architectures is crucial for achieving feasible computational and memory requirements during training and inference phases.
Job-SDF: A Multi-Granularity Dataset for Job Skill Demand Forecasting and Benchmarking Xi Chen
To bridge this gap, we present Job-SDF, a dataset designed to train and benchmark job-skill demand forecasting models.