Tag
Introduces 'Training Under Challenge', an executable-certificate framework that uses architecture-valid procedures to construct alternative models and estimate the empirical global-optimality gap of neural network checkpoints, with theoretical guarantees and experiments on ResNet-18 distillation and quantized denoising.
Introduces front-to-attractors (F2A), a new heuristic class for bidirectional search that reduces computational cost by evaluating distances to a small set of attractors instead of the full opposite frontier, achieving up to 11.2x fewer pairwise evaluations and 4.8x fewer node expansions than existing methods.