Details the MolRec system for converting chemical diagram images into MOL files using vectorization, geometric rules, and graph construction. Achieved 95% accuracy on 1000 TREC 2011 benchmark images with comprehensive failure analysis of limitations.
A 2011 rule-based OCSR system designed specifically for the challenging low-quality images in Japanese patent applications, using segment-based methods to handle pervasive problems like touching characters, merged atom labels with bonds, and broken lines.
Shannon’s foundational 1949 paper establishing the mathematical framework for modern information theory, defining channel capacity as the fundamental limit for reliable communication over noisy channels and introducing the sampling theorem (Nyquist-Shannon) that underpins all digital signal processing.
Levinthal’s 1969 perspective paper defined the protein folding paradox by demonstrating the impossibility of random search, establishing the need for kinetic pathways that guide folding faster than thermodynamic equilibration allows.
A foundational 1931 paper that derives exact recursive formulas for counting alkane structural isomers, correcting historical errors and establishing the first systematic enumeration up to C₄₀.
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.
ICML 2025 methodological paper introducing SPHNet, which uses adaptive network sparsification to overcome the computational bottleneck of tensor products in SE(3)-equivariant networks, achieving up to 7x speedup and 75% memory reduction on DFT Hamiltonian prediction tasks.
Torrie and Valleau’s 1977 paper introducing importance sampling with non-physical distributions to overcome the sampling gap problem in Monte Carlo free-energy calculations, particularly for phase transitions.
A ground-up PyTorch implementation of Word2Vec treating it as a systems engineering challenge, with “tensorized tree” architecture converting pointer-chasing Hierarchical Softmax into dense GPU operations, infinite streaming datasets with Zipfian subsampling, and torch.compile compatibility for production-grade efficiency.
Weiler et al.’s NeurIPS 2018 paper introducing 3D Steerable CNNs that achieve SE(3) equivariance through group representation theory and spherical harmonic convolution kernels, eliminating the need for rotational data augmentation and improving data efficiency for scientific applications with rotational symmetry like molecular and protein structures.
Learn to align molecular structures and point clouds using the Kabsch algorithm, with differentiable implementations for modern ML frameworks.
A PyTorch implementation enforcing strict Lyapunov stability guarantees on recurrent neural network controllers through Integral Quadratic Constraints, bridging 1990s robust control theory with modern deep reinforcement learning by solving semidefinite programs inside the gradient descent loop to provide mathematical certificates of safety.