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Asynchronous Parallel Coordinate Minimization for MAP Inference
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| adaptively reduce number of parallel workers based on estimated delay term (Proposition 1 criterion) (ablation) | APCM-Pencil · Synthetic 3-node chain graph | convergence of objective | Helped |
| increase workers from 1 to 40 with stale beliefs (ablation) | APCM-Pencil · Synthetic 3-node chain graph | convergence of objective | Hurt |
| use exact block coordinate minimization (Star updates) instead of stochastic gradient steps | APCM-Star vs HOGWILD! · Disparity estimation grid MRF | convergence speed of dual objective | Helped |
| asynchronous parallel coordinate minimization instead of HOGWILD! asynchronous SGD | MAP inference on disparity MRF · Disparity estimation grid MRF, connection widths 2/4/8 | speedup over HOGWILD! at same thread count: ~45x or higher | Helped |
| increase thread count from 1 to 46 for APCM-Star | APCM-Star · Disparity estimation grid MRF | speedup in threads | Helped |
Closest papers by meaning
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- One-Pass Algorithms for MAP Inference of Nonsymmetric Determinantal Point Processes (ICML 2022 · similarity 0.86)
- Parallel AND/OR Search for Marginal MAP (AAAI 2020 · similarity 0.86)
- MAP Estimation with Denoisers: Convergence Rates and Guarantees (NeurIPS 2025 · similarity 0.85)
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