Soft-MSM is a smooth, gradient-enabled version of the context-aware MSM distance for time series alignment that outperforms Soft-DTW alternatives in clustering and nearest-centroid classification.
The distribution of the flora in the alpine zone
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BiRD defends RAG systems against poisoning attacks by using a dual-signal framework that combines forward ranking for semantic relevance with backward ranking for context consistency, reducing attack success rates by up to 54% and improving accuracy by up to 56%.
Quantum-chemical bonding descriptors improve machine learning predictions of materials properties and enable symbolic regression to recover intuitive expressions for force constants and thermal conductivity.
citing papers explorer
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Soft-MSM: Differentiable Context-Aware Elastic Alignment for Time Series
Soft-MSM is a smooth, gradient-enabled version of the context-aware MSM distance for time series alignment that outperforms Soft-DTW alternatives in clustering and nearest-centroid classification.
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BiRD: A Bidirectional Ranking Defense Mechanism for Retrieval Augmented Generation
BiRD defends RAG systems against poisoning attacks by using a dual-signal framework that combines forward ranking for semantic relevance with backward ranking for context consistency, reducing attack success rates by up to 54% and improving accuracy by up to 56%.
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A critical assessment of bonding descriptors for predicting materials properties
Quantum-chemical bonding descriptors improve machine learning predictions of materials properties and enable symbolic regression to recover intuitive expressions for force constants and thermal conductivity.