Geoffrey Hinton’s 1984 technical report that formally derives the efficiency of distributed representations (coarse coding) and demonstrates their properties of automatic generalization, content-addressability, and robustness to damage.
An early method paper (AAAI ‘07) proposing a multi-stage sketch recognition pipeline. It introduces a domain verification step that uses chemical rules to refine ink parsing, achieving a 27% error reduction over geometric-only baselines.
A 2021 image-to-text approach treating OCSR as an image captioning task. It uses Transformers with SELFIES representation to convert molecular structure diagrams into SMILES strings, enabling extraction of visual chemical knowledge from scientific literature.
A 2024 Nature Machine Intelligence paper providing causal evidence that invalid SMILES generation improves chemical language model performance by filtering low-likelihood samples, while validity constraints (as in SELFIES) introduce structural biases that impair distribution learning.
This 2022 perspective paper reviews 250 years of chemical notation evolution and proposes 16 concrete research projects to extend SELFIES beyond traditional organic chemistry into polymers, crystals, and reactions.
Kingma and Welling’s 2013 paper introducing Variational Autoencoders and the reparameterization trick, enabling end-to-end gradient-based training of generative models with continuous latent variables by moving the stochasticity outside the computational graph so that gradients can flow through a deterministic path.
Burda et al.’s ICLR 2016 paper introducing Importance Weighted Autoencoders, which use importance sampling to derive a strictly tighter log-likelihood lower bound than standard VAEs, addressing posterior collapse and improving generative quality. The model architecture remains the same.
Discover how Importance Weighted Autoencoders (IWAEs) use the same architecture as VAEs with a different objective that optimizes a tighter bound on the log-likelihood, leveraging multiple samples effectively.
A 2023 software update paper documenting improvements to the SELFIES Python library (v2.1.1), including a streamlined context-free grammar, expanded support for aromatic systems and stereochemistry, customizable semantic constraints, ML utility functions, and performance benchmarks on 300K+ molecules.
The 2020 paper that introduced SELFIES: Mario Krenn and colleagues created a molecular representation that solves SMILES validity problems. It guarantees every generated string corresponds to a valid chemical structure.
Discover how OCSR technology bridges the gap between molecular images and machine-readable data, evolving from rule-based systems to modern deep learning models for chemical knowledge extraction.
A 2024 deep learning system for optical chemical structure recognition designed specifically for biomedical literature mining, using ResNet-Transformer architecture to handle challenging conditions including low-resolution images, noise, distortions, and even hand-drawn molecular diagrams from scientific documents.