Hypernetwork generates model parameters from one perturbed low-dimensional private dataset embedding, yielding higher utility than DP-SGD under fixed privacy budget in synthetic theory and lower FID in LoRA diffusion fine-tuning.
Learning to learn with genera- tive models of neural network checkpoints
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WIZARD meta-learns to map task evidence directly to LoRA updates for VLA policies, reporting up to 14x gains on unseen tasks in simulation and real-robot experiments without test-time optimization or action labels.
DiTs achieve SOTA FID of 2.27 on ImageNet 256x256 by scaling transformer-based latent diffusion models, with performance improving consistently as Gflops increase.
citing papers explorer
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Escaping Iterative Parameter-Space Noise: Differentially Private Learning with a Hypernetwork
Hypernetwork generates model parameters from one perturbed low-dimensional private dataset embedding, yielding higher utility than DP-SGD under fixed privacy budget in synthetic theory and lower FID in LoRA diffusion fine-tuning.
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Robotic Policy Adaptation via Weight-Space Meta-Learning
WIZARD meta-learns to map task evidence directly to LoRA updates for VLA policies, reporting up to 14x gains on unseen tasks in simulation and real-robot experiments without test-time optimization or action labels.
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Scalable Diffusion Models with Transformers
DiTs achieve SOTA FID of 2.27 on ImageNet 256x256 by scaling transformer-based latent diffusion models, with performance improving consistently as Gflops increase.