Weshowthatour temporal ensemble is asymptotically correct and our label smoothing technique can reduce the optimality gap of self-training.

However,wefind the benefit brought by self-supervised pretraining on small molecular data can be negligible in many cases.

Recently routing impr models for representing Dual-Aspect Transformer (DACT) separately potential are embedded cyclic (CPE) Transformer (i.e., cyclic design a to solve problem based impro across dif