Wonyeol Lee

Pohang University of Science and Technology — South Korea

Engineering · Physical Sciences

8h-index243citations30works1.22yr avg
OpenAlex↗ORCID↗Google Scholar↗Lab page↗

Accepting Students?

No reports yet. Know if this professor is taking students?

Research Topics

Low-power high-performance VLSI design(6), Numerical Methods and Algorithms(6), Model Reduction and Neural Networks(5), Machine Learning and Algorithms(5), Machine Learning and Data Classification(5)

Publications30 total

Optimising Density Computations in Probabilistic Programs via Automatic Loop Vectorisation
arXiv (Cornell University)·2025· 2 citedOpen Access
Random Variate Generation with Formal Guarantees
Proceedings of the ACM on Programming Languages·2025Open Access
Semantics of Integrating and Differentiating Singularities
Proceedings of the ACM on Programming Languages·2025Open Access
Floating-Point Neural Networks are Provably Robust Universal Approximators
Lecture notes in computer science·2025Open Access
Expressive power of ReLU and step networks under floating-point operations
Neural Networks·2024· 5 cited
Expressive Power of ReLU and Step Networks under Floating-Point Operations
arXiv (Cornell University)·2024Open Access
Training with Mixed-Precision Floating-Point Assignments
arXiv (Cornell University)·2023· 1 citedOpen Access
On the Correctness of Automatic Differentiation for Neural Networks with Machine-Representable Parameters
arXiv (Cornell University)·2023· 1 citedOpen Access
Smoothness Analysis for Probabilistic Programs with Application to Optimised Variational Inference
Proceedings of the ACM on Programming Languages·2023· 6 citedOpen Access
Smoothness Analysis for Probabilistic Programs with Application to Optimised Variational Inference
arXiv (Cornell University)·2022· 2 citedOpen Access
On Correctness of Automatic Differentiation for Non-Differentiable Functions
arXiv (Cornell University)·2020· 9 citedOpen Access
On Correctness of Automatic Differentiation for Non-Differentiable\n Functions
arXiv (Cornell University)·2020· 10 citedOpen Access
Differentiable Algorithm for Marginalising Changepoints
Proceedings of the AAAI Conference on Artificial Intelligence·2020Open Access
Towards verified stochastic variational inference for probabilistic programs
Proceedings of the ACM on Programming Languages·2019· 20 citedOpen Access
Differentiable Algorithm for Marginalising Changepoints
arXiv (Cornell University)·2019Open Access
Towards Verified Stochastic Variational Inference for Probabilistic Programs
arXiv (Cornell University)·2019Open Access
Reparameterization Gradient for Non-differentiable Models
arXiv (Cornell University)·2018· 7 citedOpen Access
On Automatically Proving the Correctness of math.h Implementations
Symposium on Principles of Programming Languages·2018· 4 cited
Reparameterization Gradient for Non-differentiable Models
arXiv (Cornell University)·2018· 4 citedOpen Access
On automatically proving the correctness of math.h implementations
Proceedings of the ACM on Programming Languages·2017· 27 citedOpen Access
page 1 of 2Next →

Frequent Co-authors

Hongseok Yang(11), Xavier Rival(7), Alex Aiken(6), Hangyeol Yu(6), Rahul Sharma(4), Sejun Park(4), Geonho Hwang(3), Jinha Kim(2), Hwanjo Yu(2), Sungwoo Park(2), Yeachan Park(2), Hyoung-Jin Lim(2), Gwonsoo Che(2), Feras A. Saad(2), Hong Seok Yang(1), Se Yun Kim(1), Young Woo Kim(1), Jae Young Lim(1), Dong-Min Lim(1), Seung-Chul Lim(1)