COM integrates geometric constraints into token initialization and training to preserve continuity and ordinality in time series tokens, improving token-based TS-LLM performance on benchmarks.
Timemae: Self-supervised rep- resentations of time series with decoupled masked autoen- coders
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
representative 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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Continuity and Ordinality Matter: Constraining Time Series Tokens for Effective Time Series Analysis with Large Language Models
COM integrates geometric constraints into token initialization and training to preserve continuity and ordinality in time series tokens, improving token-based TS-LLM performance on benchmarks.
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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.