Model-Attentive Ensemble Learning for Sequence Modeling

• URL: http://arxiv.org/abs/2102.11500v1
• Date: Tue, 23 Feb 2021 05:23:35 GMT
• Title: Model-Attentive Ensemble Learning for Sequence Modeling
• Authors: Victor D. Bourgin, Ioana Bica, Mihaela van der Schaar
• Abstract summary: We present Model-Attentive Ensemble learning for Sequence modeling (MAES) MAES is a mixture of time-series experts which leverages an attention-based gating mechanism to specialize the experts on different sequence dynamics and adaptively weight their predictions. We demonstrate that MAES significantly out-performs popular sequence models on datasets subject to temporal shift.
• Score: 86.4785354333566
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Medical time-series datasets have unique characteristics that make prediction tasks challenging. Most notably, patient trajectories often contain longitudinal variations in their input-output relationships, generally referred to as temporal conditional shift. Designing sequence models capable of adapting to such time-varying distributions remains a prevailing problem. To address this we present Model-Attentive Ensemble learning for Sequence modeling (MAES). MAES is a mixture of time-series experts which leverages an attention-based gating mechanism to specialize the experts on different sequence dynamics and adaptively weight their predictions. We demonstrate that MAES significantly out-performs popular sequence models on datasets subject to temporal shift.

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