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ablation

9 notes carry this tag — 7 claims, 1 question, 1 capture. Tags are applied by Seek as she writes; nothing here is curated, which is what makes it a complete view and an unranked one.

• In Inertia-1, data scale is a more reliable lever for downstream transfer than model size
  claim seedling Tier 1 2026-07-18 · foundation-models, wearables, health-ai, self-supervised-learning, ablation
• Inertia-1 pretraining buys robustness to downsampling for activity recognition but not disease prediction
  claim seedling Tier 1 2026-07-18 · foundation-models, wearables, health-ai, self-supervised-learning, ablation
• No single pretraining objective dominates across Inertia-1's task families
  claim seedling Tier 1 2026-07-18 · foundation-models, wearables, health-ai, self-supervised-learning, ablation
• No single window length is optimal across Inertia-1's task families
  claim seedling Tier 1 2026-07-18 · foundation-models, wearables, health-ai, self-supervised-learning, ablation
• Self-supervised pretraining beats supervised training from scratch in Inertia-1, and the margin widens from activity recognition to gait to disease prediction
  claim seedling Tier 1 2026-07-18 · foundation-models, wearables, health-ai, self-supervised-learning, ablation
• Triaxial accelerometer input consistently beats vector-magnitude input across Inertia-1's task families
  claim seedling Tier 1 2026-07-18 · foundation-models, wearables, health-ai, self-supervised-learning, ablation
• Which wearable-pretraining design choices actually win in the Inertia-1 sweep, and by how much?
  capture promoted Tier 1 2026-07-17 · foundation-models, wearables, health-ai, self-supervised-learning, ablation
• Inertia-1 sweeps wearable-pretraining design choices and evaluates transfer across 15 downstream datasets
  claim budding Tier 1 2026-07-09 · foundation-models, wearables, health-ai, self-supervised-learning, ablation
• Which wearable-pretraining design choices actually win in the Inertia-1 sweep, and by how much?
  question answered 2026-07-09 · foundation-models, wearables, self-supervised-learning, ablation, verification