Proposes a unified statistical framework for recognizing both inorganic and organic handwritten chemical expressions. Introduces the Chemical Expression Structure Graph (CESG) and uses a weighted direction graph search for structural analysis, achieving 83.1% top-5 accuracy on a large proprietary dataset.
This paper introduces MLOCSR, a system that pipelines low-level image vectorization with a high-level probabilistic Markov Logic Network to recognize chemical structures. It replaces brittle heuristics with weighted logic rules, significantly outperforming state-of-the-art systems like OSRA on degraded or low-resolution images.
ChemInk introduces a sketch recognition system for chemical diagrams that combines multi-level visual features via a joint Conditional Random Field (CRF), achieving 97.4% accuracy and outperforming CAD tools in user speed.
Geoffrey Hinton’s seminal 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, unlocking visual chemical knowledge from scientific literature.
This paper introduces the Hierarchical Ideal Point Topic Model (HIPTM) to analyze the 112th U.S. Congress. By jointly modeling votes and text, it uncovers how Tea Party Republicans and establishment Republicans differ in both voting records and how they frame specific policy issues.
Kingma and Welling’s foundational 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.
Discover how Importance Weighted Autoencoders (IWAEs) use the same architecture as VAEs with a fundamentally more powerful objective to leverage multiple samples effectively.
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.
A 2023 software update paper documenting major improvements to the SELFIES Python library, including architectural redesign using directed molecular graphs for faster performance, expanded chemical feature support, semantic constraints for validity, and user-friendly customization APIs that transform SELFIES from proof-of-concept into production-ready tool.
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.