4 papers
Riemannian Batch Normalization: A Gyro Approach
Ziheng Chen, Xiao-Jun Wu, Bernhard Schölkopf +1
Normalization layers are crucial for deep learning, but their Euclidean formulations are inadequate for data on manifolds. On the other hand, many Riemannian manifolds in machine l…
Generalized Interpolating Discrete Diffusion
Dimitri von Rütte, Janis Fluri, Yuhui Ding +3
While state-of-the-art language models achieve impressive results through next-token prediction, they have inherent limitations such as the inability to revise already generated to…
DiffRatio: Training One-Step Diffusion Models Without Teacher Supervision
Wenlin Chen, Mingtian Zhang, Jiajun He +4
Score-based distillation methods (e.g., variational score distillation) train one-step diffusion models by first pre-training a teacher score model and then distilling it into a on…
First-order Adversarial Vulnerability of Neural Networks and Input Dimension
Carl-Johann Simon-Gabriel, Yann Ollivier, Léon Bottou +2
Over the past few years, neural networks were proven vulnerable to adversarial images: targeted but imperceptible image perturbations lead to drastically different predictions. We…