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Tight Risk Bounds for Gradient Descent on Separable Data
Recently, there has been a marked increase in interest regarding the generalization capabilities of unregularized gradient-based learning methods.
Tight Risk Bounds for Gradient Descent on Separable Data
Recently, there has been a marked increase in interest regarding the generalization capabilities of unregularized gradient-based learning methods.
Reverse Transition Kernel: A Flexible Framework to Accelerate Diffusion Inference
This gives rise to the RTK-MALA and RTK-ULD algorithms for diffusion inference.
ASPEN: Breaking Operator Barriers for Efficient Parallel Execution of Deep Neural Networks
ASPEN also achieves high resource utilization and memory reuse by letting each resource asynchronously traverse depthwise in the DNN graph to its full computing potential.
Diffusion-Reward Adversarial Imitation Learning
Despite its encouraging results, GAIL training is often brittle and unstable.
