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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AD-HMC achieves geometric convergence in Wasserstein distance for HMC with general asymmetrical auxiliary momentum distributions by restoring self-adjointness via direction alternation, with extensions to leapfrog integrators.
VarWISE catalog identifies 457,080 high-confidence infrared variables (49.81% new) and an extended set of 1.9 million from NEOWISE photometry via spatial clustering, VARnet detection, and XGBoost classification.
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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Hamiltonian Monte Carlo with Asymmetrical Momentum Distributions
AD-HMC achieves geometric convergence in Wasserstein distance for HMC with general asymmetrical auxiliary momentum distributions by restoring self-adjointness via direction alternation, with extensions to leapfrog integrators.
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VarWISE: Infrared Variability via NEOWISE Single Exposure Photometry
VarWISE catalog identifies 457,080 high-confidence infrared variables (49.81% new) and an extended set of 1.9 million from NEOWISE photometry via spatial clustering, VARnet detection, and XGBoost classification.