Edge detection using morphological amoebas in noisy images. - Wonyeol Lee 001; Se Yun Kim; Young Woo Kim; Jae Young Lim; Dong Hoon Lim (ICIP, 2009)

Edge detection based on morphological amoebas - Wonyeol Lee 001; Young Woo Kim; Se Yun Kim; Jae Young Lim; Dong Hoon Lim (CoRR, 2011)

A proof system for separation logic with magic wand. - Wonyeol Lee 001; Sungwoo Park (POPL, 2014)

CT-IC: Continuously activated and Time-restricted Independent Cascade model for viral marketing. - Jinha Kim; Wonyeol Lee 001; Hwanjo Yu (Knowl. Based Syst., 2014)

Verifying bit-manipulations of floating-point. - Wonyeol Lee 001; Rahul Sharma 001; Alex Aiken (PLDI, 2016)

Reparameterization Gradient for Non-differentiable Models. - Wonyeol Lee 001; Hangyeol Yu; Hongseok Yang (NeurIPS, 2018)

On automatically proving the correctness of math.h implementations. - Wonyeol Lee 001; Rahul Sharma 001; Alex Aiken (Proc. ACM Program. Lang., 2018)

Differentiable Algorithm for Marginalising Changepoints. - Hyoungjin Lim; Gwonsoo Che; Wonyeol Lee 001; Hongseok Yang (AAAI, 2020)

On Correctness of Automatic Differentiation for Non-Differentiable Functions. - Wonyeol Lee 001; Hangyeol Yu; Xavier Rival; Hongseok Yang (NeurIPS, 2020)

Towards verified stochastic variational inference for probabilistic programs. - Wonyeol Lee 001; Hangyeol Yu; Xavier Rival; Hongseok Yang (Proc. ACM Program. Lang., 2020)

On the Correctness of Automatic Differentiation for Neural Networks with Machine-Representable Parameters. - Wonyeol Lee 001; Sejun Park; Alex Aiken (ICML, 2023)

Training with Mixed-Precision Floating-Point Assignments. - Wonyeol Lee 001; Rahul Sharma 001; Alex Aiken (Trans. Mach. Learn. Res., 2023)

Smoothness Analysis for Probabilistic Programs with Application to Optimised Variational Inference. - Wonyeol Lee 001; Xavier Rival; Hongseok Yang (Proc. ACM Program. Lang., 2023)

What does automatic differentiation compute for neural networks? - Sejun Park; Sanghyuk Chun; Wonyeol Lee 001 (ICLR, 2024)

Expressive power of ReLU and step networks under floating-point operations. - Yeachan Park; Geonho Hwang; Wonyeol Lee 001; Sejun Park (Neural Networks, 2024)

Floating-Point Neural Networks are Provably Robust Universal Approximators. - Geonho Hwang; Wonyeol Lee 001; Yeachan Park; Sejun Park; Feras Saad (CAV (2), 2025)

Semantics of Integrating and Differentiating Singularities. - Jesse Michel; Wonyeol Lee 001; Hongseok Yang (Proc. ACM Program. Lang., 2025)

Random Variate Generation with Formal Guarantees. - Feras A. Saad; Wonyeol Lee 001 (Proc. ACM Program. Lang., 2025)