Di-COT is an unsupervised contrastive method that stochastically partitions time-series windows into overlapping sub-blocks to learn representations without augmentation, reporting SOTA results on classification and transfer tasks across multiple benchmarks while cutting training time.
Journal of Machine learning research , volume=
2 Pith papers cite this work. Polarity classification is still indexing.
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Sensitivity analysis of tactical wireless network design via Tabu Search reveals scale-dependent transitions where some parameters reshape topology while others mainly scale performance magnitude.
citing papers explorer
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Divide and Contrast: Learning Robust Temporal Features without Augmentation
Di-COT is an unsupervised contrastive method that stochastically partitions time-series windows into overlapping sub-blocks to learn representations without augmentation, reporting SOTA results on classification and transfer tasks across multiple benchmarks while cutting training time.
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Sensitivity Analysis of Tactical Wireless Network Design Under Realistic Operational Constraints
Sensitivity analysis of tactical wireless network design via Tabu Search reveals scale-dependent transitions where some parameters reshape topology while others mainly scale performance magnitude.