sGPO uses an initial-policy success-rate profiling pass to adaptively set rollout group sizes, filter data, and build a curriculum, cutting total RLVR training compute by 3x while matching baseline performance.
& Balasubra- manian, V
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XWP and XWP_c are novel attribution methods for FCNNs that estimate feature importance by perturbing attached weights to avoid added bias and out-of-distribution issues in occlusion approaches.
Quantum annealing solves a combinatorial feature-map selection problem for CNNs, yielding improved class disentanglement over GradCAM and GradCAM++ in the reported evaluation.
LTBs-KAN delivers linear-time B-spline evaluation in KANs plus parameter reduction via product-of-sums factorization, with competitive results on MNIST, Fashion-MNIST, and CIFAR-10.
Introduces a unified evaluation framework for XAI using five principled metrics and the PGCA method that fuses grid perturbation with Grad-CAM++ , reporting top scores in fidelity, interpretability and fairness on ResNet-50 models across five image domains.
Realistic noise synthesis incorporating Rician expectation and effective variance into simulated training data reduces bias in supervised ML for diffusion MRI microstructure estimation.
Model interpretation methods are reformulated to emphasize baselines; gradient-based methods, IG, and Taylor expansion are unified with explicit baselines identified, and a revised IG is developed for improved results from any layer.
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