Consistency models achieve fast one-step generation with SOTA FID of 3.55 on CIFAR-10 and 6.20 on ImageNet 64x64 by directly mapping noise to data, outperforming prior distillation techniques.
Learning multiple layers of features from tiny images
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SOLAR prevents latent rehearsal decay in online continual SSL by adaptively managing replay buffers with deviation proxies and an explicit overlap loss, delivering both fast convergence and state-of-the-art final accuracy on vision benchmarks.
Verification of machine unlearning is fragile because model providers can use adversarial unlearning to pass checks while keeping data influence.
Grasp is a grid search method with self-bounding analysis enabling fully parameter-free stochastic optimization with near-optimal rates in non-convex settings and competitive performance in convex cases.
Representations learned by large AI models are converging toward a shared statistical model of reality.
citing papers explorer
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Consistency Models
Consistency models achieve fast one-step generation with SOTA FID of 3.55 on CIFAR-10 and 6.20 on ImageNet 64x64 by directly mapping noise to data, outperforming prior distillation techniques.
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Preventing Latent Rehearsal Decay in Online Continual SSL with SOLAR
SOLAR prevents latent rehearsal decay in online continual SSL by adaptively managing replay buffers with deviation proxies and an explicit overlap loss, delivering both fast convergence and state-of-the-art final accuracy on vision benchmarks.
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Verification of Machine Unlearning is Fragile
Verification of machine unlearning is fragile because model providers can use adversarial unlearning to pass checks while keeping data influence.
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Towards Fully Parameter-Free Stochastic Optimization: Grid Search with Self-Bounding Analysis
Grasp is a grid search method with self-bounding analysis enabling fully parameter-free stochastic optimization with near-optimal rates in non-convex settings and competitive performance in convex cases.
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The Platonic Representation Hypothesis
Representations learned by large AI models are converging toward a shared statistical model of reality.