Discriminator Guidance for Autoregressive Diffusion Models

We introduce discriminator guidance in the setting of Autoregressive Diffusion Models. The use of a discriminator to guide a diffusion process has previously been used for continuous diffusion models, and in this work we derive ways of using a discriminator together with a pretrained generative model in the discrete case. First, we show that using an optimal discriminator will correct the pretrained model and enable exact sampling from the underlying data distribution. Second, to account for the realistic scenario of using a sub-optimal discriminator, we derive a sequential Monte Carlo algorithm which iteratively takes the predictions from the discrimiator into account during the generation process. We test these approaches on the task of generating molecular graphs and show how the discriminator improves the generative performance over using only the pretrained model.

Accelerating Molecular Graph Neural Networks via Knowledge Distillation

Recent advances in graph neural networks (GNNs) have allowed molecular simulations with accuracy on par with conventional gold-standard methods at a fraction of the computational cost. Nonetheless, as the field has been progressing to bigger and more complex architectures, state-of-the-art GNNs have become largely prohibitive for many large-scale applications. In this paper, we, for the first time, explore the utility of knowledge distillation (KD) for accelerating molecular GNNs. To this end, we devise KD strategies that facilitate the distillation of hidden representations in directional and equivariant GNNs and evaluate their performance on the regression task of energy and force prediction. We validate our protocols across different teacher-student configurations and demonstrate that they can boost the predictive accuracy of student models without altering their architecture. We also conduct comprehensive optimization of various components of our framework, and investigate the potential of data augmentation to further enhance performance. All in all, we manage to close as much as 59% of the gap in predictive accuracy between models like GemNet-OC and PaiNN with zero additional cost at inference.