A novel complexity minimization meta-learning framework provably demonstrates that few-shot adaptation error decreases as meta-training data volume increases.
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NDR-SHKF replaces the static forgetting factor in Sage-Husa Kalman Filters with a learned vector-valued memory attenuation policy from a bifurcated recurrent network trained end-to-end on whitened innovations to minimize estimation error.
Defines saturation index S(K) = erank(Σ̂_W^(K))/K that identifies when linear discriminant stabilizes in binary few-shot classification, with empirical phase diagram and stopping-rule AUC of 0.752 on 17 tasks.
DeconDTN-Toolkit simulates provenance shifts to expose ERM vulnerabilities and provides tools plus a robust OOD indicator for mitigating confounding by data provenance.
Including copying tasks in training enables transformers to learn letter-string analogies, improving generalization to new alphabets with a 3-layer model outperforming some frontier models.
SHINE trains a scalable in-context hypernetwork to generate high-quality LoRA adapters from contexts in one pass, enabling efficient LLM adaptation that saves time and compute compared to standard fine-tuning.
A hybrid genetic algorithm with model transformations generates families of RL training environments, demonstrated for wildfire mitigation and curriculum learning.
A systematic evaluation of GPU memory and utilization estimators across analytical, library-based, and ML paradigms identifies key limitations in generalization, integration overhead, and hardware variability for training-aware resource management.