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MVLAD-AD combines masked vision-language-action diffusion with discrete action tokenization, geometry-aware embeddings, and action-priority decoding for efficient, precise, and explainable end-to-end autonomous driving.

Self-Distilled One-Step Diffusion Samplers achieve fast one/few-step sampling and stable evidence estimation through state-space and volume consistency with a deterministic-flow importance weight.

Diffusion Models (DMs) have evolved into advanced image generation tools, especially for few-shot fine-tuning where a pretrained DM is …

In this paper, we propose a novel DNN-based method for supervised causal learning that addresses systematic biases in existing methods, with a newly designed pairwise encoder serving as the core architecture.

in this paper, we are the first to explore and propose to utilize adversarial examples for DMs to protect human-created artworks. Specifically, we first build a theoretical framework to define and evaluate the adversarial examples for DMs. Then, based on this framework, we design a novel algorithm, named AdvDM, which exploits a Monte-Carlo estimation of adversarial examples for DMs by optimizing upon different latent variables sampled from the reverse process of DMs.

In this paper, we propose Information Bound as a metric of the amount of information in Bayesian neural networks. Different from mutual information on deterministic neural networks where modification of network structure or specific input data is usually necessary, Information Bound can be easily estimated on current Bayesian neural networks without any modification of network structures or training processes.