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Deep Temporal Sets with Evidential Reinforced Attentions for Unique Behavioral Pattern Discovery

ICML 2023 · Vision-language and multimodal › Human and embodied modeling · approach: Multimodal and affective interaction modeling

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
add evidential reinforced attention (ERA) to DTS (ablation)DTS · Maze-Mixedaccuracy: 92.7 to 95.0Helped
add evidential reinforced attention (ERA) to DTS (ablation)DTS · Maze-2Daccuracy: 93.1 to 94.0Helped
replace RL-based ERA with standard ANP attention on DTS (ablation)DTS · Maze-Mixedaccuracy: 95.0 to 91.8Hurt
use only ERA-selected attentive embedding without concatenating full DTS embedding (ablation)DTS-ERA · Maze-Mixedaccuracy: 95.0 to 92.4Hurt
use DTS set-based encoder instead of LSTM recurrent encoderLSTM baseline · Maze-Mixedaccuracy: 68.8 to 92.7Helped
add evidential reinforced attention on top of DTS (DTS-ERA) vs baselinesDTS-ERA · Coloringaccuracy: 91.0 vs 88.0 (best baseline GRU-FCN)Helped

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