Research map / Vision-language and multimodal / Human and embodied modeling
Deep Temporal Sets with Evidential Reinforced Attentions for Unique Behavioral Pattern Discovery
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add evidential reinforced attention (ERA) to DTS (ablation) | DTS · Maze-Mixed | accuracy: 92.7 to 95.0 | Helped |
| add evidential reinforced attention (ERA) to DTS (ablation) | DTS · Maze-2D | accuracy: 93.1 to 94.0 | Helped |
| replace RL-based ERA with standard ANP attention on DTS (ablation) | DTS · Maze-Mixed | accuracy: 95.0 to 91.8 | Hurt |
| use only ERA-selected attentive embedding without concatenating full DTS embedding (ablation) | DTS-ERA · Maze-Mixed | accuracy: 95.0 to 92.4 | Hurt |
| use DTS set-based encoder instead of LSTM recurrent encoder | LSTM baseline · Maze-Mixed | accuracy: 68.8 to 92.7 | Helped |
| add evidential reinforced attention on top of DTS (DTS-ERA) vs baselines | DTS-ERA · Coloring | accuracy: 91.0 vs 88.0 (best baseline GRU-FCN) | Helped |
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