Introduces PowerPhase benchmark for massive-variate power-system forecasting and PowerForge model that achieves best average rank on safety-fidelity metrics across all tested grids.
ScoreGrad : Multivariate probabilistic time series forecasting with continuous energy-based generative models
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
years
2026 4verdicts
UNVERDICTED 4representative citing papers
TooBad achieves >85% ASR on diffusion models at 0.5% poison rate and near-100% ASR at 5% poison rate in 3-5 epochs while evading SOTA defenses, far below the 10% rates of prior work.
DeRegiME uses a sparse variational GP with nonstationary regime-mixing kernel to decompose forecasts into mean, residual regimes, and noise for improved probabilistic forecasting under distribution shift.
HyFAD combines sequential time-to-frequency diffusion with frequency-aware step embeddings to achieve claimed state-of-the-art time series imputation on benchmarks.
citing papers explorer
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Navigating the Safety-Fidelity Trade-off: Massive-Variate Time Series Forecasting for Power Systems via Probabilistic Scenarios
Introduces PowerPhase benchmark for massive-variate power-system forecasting and PowerForge model that achieves best average rank on safety-fidelity metrics across all tested grids.
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TooBad: Backdoor Diffusion Models with Ultra-Low Poison Rate and Imperceptible Trigger
TooBad achieves >85% ASR on diffusion models at 0.5% poison rate and near-100% ASR at 5% poison rate in 3-5 epochs while evading SOTA defenses, far below the 10% rates of prior work.
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DeRegiME: Deep Regime Mixtures for Probabilistic Forecasting under Distribution Shift
DeRegiME uses a sparse variational GP with nonstationary regime-mixing kernel to decompose forecasts into mean, residual regimes, and noise for improved probabilistic forecasting under distribution shift.
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HyFAD: Hybrid Time-Frequency Diffusion with Frequency-Aware Embedding for Time Series Imputation
HyFAD combines sequential time-to-frequency diffusion with frequency-aware step embeddings to achieve claimed state-of-the-art time series imputation on benchmarks.