Molecular Representation Learning (MRL) has emerged as a powerful tool for drug and materials discovery in a variety of tasks such as virtual screening and inverse design. While there has been a surge of interest in advancing modelcentric techniques, the influence of both data quantity and quality on molecular representations is not yet clearly understood within this field.

This gives rise to a rich collection oftopological relationships and conditions under which VE models are optimal for planning. Despite this effort, relatively little is known about the planning performance of models that fail to satisfy these conditions.

From Figure 1(a), learning multiple linear sub-models and averaging the predictions (ensemble) is still a linear model, so it cannot tackleXOR problem. We compare the training cost of all methods from the two aspects;1). Thesub-model training enables themost adversarial attacks ofsub-models could be successfully defended. In particular, we train two kinds of models to defend against the attacks: 1). FromFigure2(a)and2(b),when0.01 ϵ 0.04, SoE without the collaboration training achieves a similar robustness compared with SoE.

Inmany cases, increasing model capacity beyond the memory limit of a single acceleratorhas required developing special algorithms orinfrastructure. These solutions are often architecture-specific and do not transfer to other tasks.

While capable of generating photo-realistic images, these models often produce unrealistic samples which fall outside of the data manifold.