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Characterizing the Loss Landscape in Non-Negative Matrix Factorization

AAAI 2021 · Optimization › Implicit bias and generalization · approach: Edge-of-stability dynamics · 1 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Increase ResNet width from k=1 to k=4 (ablation)ResNet-32 · CIFAR10, epoch 300convexity length scale: 0.55 ˘ 0.037 -> 0.82 ˘ 0.032Helped
Increase ResNet width from k=1 to k=4 (ablation)ResNet-44 · CIFAR10, epoch 300convexity length scale: 0.57 ˘ 0.036 -> 0.78 ˘ 0.032Helped
Increase ResNet width from k=1 to k=4 (ablation)ResNet-56 · CIFAR10, epoch 300convexity length scale: 0.57 ˘ 0.035 -> 0.79 ˘ 0.033Helped
Increase ResNet width from k=1 to k=4 (ablation)ResNet-68 · CIFAR10, epoch 300convexity length scale: 0.58 ˘ 0.036 -> 0.81 ˘ 0.033Helped
Increase ResNet width from k=1 to k=4 (ablation)ResNet-80 · CIFAR10, epoch 300convexity length scale: 0.63 ˘ 0.036 -> 0.79 ˘ 0.03Helped
Increase ResNet width from k=1 to k=4 (ablation)ResNet-110 · CIFAR10, epoch 300convexity length scale: 0.63 ˘ 0.037 -> 0.82 ˘ 0.03Helped
Increase the sampled NMF matrix size by using more rows (ablation)NMF · Birdsong, MovieLens, satellite, ORL facesfraction of sampled curvatures that are positiveNo clear effect
Increase the sampled NMF matrix size by using more rows (ablation)NMF · Birdsong, MovieLens, satellite, ORL facesrelative deviation of curvatureHelped

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