I aligned a vision model into a frozen chemistry model’s embedding space two ways, contrastive and predictive, and held everything else identical. Four things surprised me, including a metric defect that produced exactly the result I had registered in advance.
We explore the ‘Silent Failure’ mode of LLMs in production: the limits of 99% accuracy for reliability, how confidence decays in long documents, and why standard calibration techniques struggle to fix it.
We trace the history of Page Stream Segmentation (PSS) through three eras (Heuristic, Encoder, and Decoder) and explain how privacy-preserving, localized LLMs enable true semantic processing.
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.
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.
Build a Python CLI tool that converts SMILES and SELFIES notation into 2D molecular images with chemical formulas and legends, including an SVG path for figures.
Explore two fundamental approaches to generating exponentially distributed random numbers: the modern inverse transform method using logarithms and von Neumann’s ingenious 1951 comparison-based algorithm that avoids transcendental functions entirely.
Step-by-step implementation of the classic Müller-Brown potential in PyTorch, with performance comparisons between analytical and automatic differentiation approaches for molecular dynamics and machine learning applications.
I replicated Rahman’s landmark 1964 liquid argon molecular dynamics simulation using modern tools, building a Python analysis pipeline with caching, vectorization, and type hints to bridge vintage science with modern software engineering.
Get a practical overview of the GEOM dataset and learn how it’s advancing 3D molecular machine learning by bridging static graphs and dynamic reality.
A complete guide to implementing modern Variational Autoencoders in PyTorch. Includes a copy-pasteable implementation, explanation of KL annealing to fix posterior collapse, and a deep dive into stable standard deviation parameterizations.
What happens when you achieve 99.8% accuracy on sarcasm detection? You might have accidentally built a domain classifier. A cautionary ML tale about dataset bias.