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Nearly a third of variable-wise gradients directly oppose each other during standard multivariate forecasting, but resolving these conflicts via single-pass gradient surgery reliably boosts accuracy without multiple backward passes.
Active learning can boost unsupervised anomaly detection performance by over 12% in complex time series data, tackling the challenge of subtle anomalies head-on.
Spot anomalies before they happen: FATE uses disagreement among time-series forecasting models to provide early warning signals without needing labeled anomaly data.
