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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2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 2verdicts
UNVERDICTED 2representative citing papers
The paper proposes persistent caching of component data and adapted branching heuristics to amortize computation in incremental #SAT, showing performance gains on argumentation and soft core problems.
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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Efficient Incremental #SAT via Cross-Instance Knowledge Reuse
The paper proposes persistent caching of component data and adapted branching heuristics to amortize computation in incremental #SAT, showing performance gains on argumentation and soft core problems.