Unitho: A Unified Multi-Task Framework for Computational Lithography

ChemFixer is a new framework designed to correct invalid molecules generated by deep learning-based molecular generation models. These models have shown promise in exploring chemical spaces for potential drug candidates, but often produce chemically invalid outputs. ChemFixer utilizes a transformer architecture and is fine-tuned on a dataset of valid and invalid molecular pairs. Evaluations indicate that it enhances molecular validity while maintaining the chemical and biological properties of the original outputs, thus expanding the usable chemical space.

The paper introduces a novel likelihood-guided variational Ising-based regularization framework for Vision Transformers (ViTs), aimed at enhancing model generalization while dynamically pruning redundant parameters. This approach utilizes Bayesian sparsification techniques to impose structured sparsity on model weights, allowing for adaptive architecture search during training. Unlike traditional dropout methods, this framework learns task-adaptive regularization, improving efficiency and interpretability in classification tasks involving structured and high-dimensional data.