Time-R1 trains LLMs via supervised fine-tuning followed by reinforcement learning with a time-series-specific reward and non-uniform GRIP sampling to enable multi-step reasoning that improves forecasting accuracy.
Timemae: Self-supervised rep- resentations of time series with decoupled masked autoen- coders
2 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 2representative citing papers
DT-Pose reformulates WiFi HPE as domain-consistent representation learning via temporal contrastive masked pretraining plus hybrid topology-constrained decoding to yield more accurate and realistic 2D/3D poses.
citing papers explorer
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Time Series Forecasting as Reasoning: A Slow-Thinking Approach with Reinforced LLMs
Time-R1 trains LLMs via supervised fine-tuning followed by reinforcement learning with a time-series-specific reward and non-uniform GRIP sampling to enable multi-step reasoning that improves forecasting accuracy.
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Towards Robust and Realistic Human Pose Estimation via WiFi Signals
DT-Pose reformulates WiFi HPE as domain-consistent representation learning via temporal contrastive masked pretraining plus hybrid topology-constrained decoding to yield more accurate and realistic 2D/3D poses.