Author

Zachary Shinnick

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#machine learning Preprint Sep 2026

Lasting Effects of Abstract Pretraining Beyond Perplexity

Language models are typically pretrained from random initialization. Recent work challenges this convention, showing that a brief warm-up on abstract, algorithmically generated data can provide a better starting point for subsequent learning of natural language. In this paper, we show that in small language models, suc...

Zachary Shinnick, Hemanth Saratchandran, Damien Teney et al. · 0 citations

#artificial intelligence Preprint Sep 2026

Procedural Pretraining for Molecular Property Prediction

It is found that procedural pretraining can improve molecular property prediction even after subsequent molecular pretraining, and procedural data can provide transferable structure for molecular learning and offer a complementary route to improving performance when labeled molecular data are limited.

M. Friedemann, Zachary Shinnick, Philip H. S. Torr et al. · 0 citations