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Found 5 papers, 1 papers with code
Matching the Statistical Query Lower Bound for k-sparse Parity Problems with Stochastic Gradient Descent
We then demonstrate how a trained neural network with SGD can effectively approximate this good network, solving the $k$-parity problem with small statistical errors.
Guided Discrete Diffusion for Electronic Health Record Generation
Electronic health records (EHRs) are a pivotal data source that enables numerous applications in computational medicine, e. g., disease progression prediction, clinical trial design, and health economics and outcomes research.
Fast Sampling via De-randomization for Discrete Diffusion Models
Despite its success in continuous spaces, discrete diffusion models, which apply to domains such as texts and natural languages, remain under-studied and often suffer from slow generation speed.
Benign Overfitting for Two-layer ReLU Convolutional Neural Networks
We show that, under mild conditions, the neural network trained by gradient descent can achieve near-zero training loss and Bayes optimal test risk.
Certified Adversarial Robustness Under the Bounded Support Set
In this paper, we introduce a framework that is able to deal with robustness properties of arbitrary smoothing measures including those with bounded support set by using Wasserstein distance as well as total variation distance.
