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Multimodal fusion research on StripNet+GTA-UAV proxy: - 3 independent fusion tracks: condition-aware (A), token/bottleneck (B), role-aware (C) - Shared interfaces, protocol, dataset audit, baseline benchmarks - Canonical version-chain references to vault (SPEC, ANALYSIS, TRIAGE) - Personalized task plans and decision tables for each researcher - 3 generated DOCX task assignment files with milestones and DoD checklist - Full modality dropout diagnostics and missing-modality robustness requirements - Data contract, benchmark registry, experiment tracking infrastructure Operational documents: - docs/00_project/: MERIDIAN context, protocol, repository reuse guide, experiment specification - docs/01_tasks/: Master assignment + 3 individual researcher tracks + joint integration - docs/02_references/: Core literature, version-chain bases, code maps - docs/03_codebase_guides/: Existing code snapshots from vault - scripts/: gen_task_plans.js (DOCX generation), placeholder infrastructure - vendor_reference/: Snapshots of caption_test, depth_edges_annotate, existing SOFIA/SegModel code - reports/, results/, experiments/: Shared output structure for all 3 researchers 3 DOCX files generated from gen_task_plans.js (Times New Roman 14pt, GOST format): - План_заданий_Павленко_БВ.docx (Condition-Aware track, fusion API owner) - План_заданий_Близно_МВ.docx (Token/Bottleneck track, benchmark owner) - План_заданий_Мороз_ЕС.docx (Role-Aware track, data contract owner) Co-Authored-By: Claude Haiku 4.5 <noreply@anthropic.com>
14 lines
422 B
Plaintext
14 lines
422 B
Plaintext
# Common optimization settings.
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TrainingConfig.output_root = 'out'
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TrainingConfig.seed = 42
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TrainingConfig.epochs = 10
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TrainingConfig.batch_size = 8
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TrainingConfig.num_workers = 4
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TrainingConfig.learning_rate = 0.0001
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TrainingConfig.weight_decay = 0.0001
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TrainingConfig.gradient_clip = 1.0
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TrainingConfig.tau_init = 0.07
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TrainingConfig.label_smoothing = 0.1
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TrainingConfig.weight_q2g = 0.6
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TrainingConfig.weight_g2q = 0.4
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