Geonho Hwang - RankMe

24 papers A* 5Journal 18Unranked 1

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	On the Expressive Power of Floating-Point Transformers. CoRR Sejun Park, Yeachan Park, Geonho Hwang
2025	J	`jnl`	Analyzing the latent space of GAN through local dimension estimation for disentanglement evaluation. Pattern Recognit. Jaewoong Choi, Geonho Hwang, Hyunsoo Cho, Myungjoo Kang
2025	J	`jnl`	Floating-Point Neural Networks Are Provably Robust Universal Approximators. CoRR Geonho Hwang, Wonyeol Lee, Yeachan Park, Sejun Park, Feras Saad
2025	A*	`conf`	Floating-Point Neural Networks Can Represent Almost All Floating-Point Functions. ICML Geonho Hwang, Yeachan Park, Wonyeol Lee, Sejun Park
2025	—	`conf`	Floating-Point Neural Networks are Provably Robust Universal Approximators. CAV (2) Geonho Hwang, Wonyeol Lee, Yeachan Park, Sejun Park, Feras Saad
2025	J	`jnl`	Localized Estimation of Condition Numbers for MILU Preconditioners on a Graph. CoRR Geonho Hwang, Yesom Park, Yueun Lee, Jooyoung Hahn, Myungjoo Kang
2025	A*	`conf`	Minimum Width for Universal Approximation using Squashable Activation Functions. ICML Jonghyun Shin, Namjun Kim, Geonho Hwang, Sejun Park
2025	J	`jnl`	Minimum width for universal approximation using squashable activation functions. CoRR Jonghyun Shin, Namjun Kim, Geonho Hwang, Sejun Park
2024	J	`jnl`	Analysis of efficient preconditioner for solving Poisson equation with Dirichlet boundary condition in irregular three-dimensional domains. J. Comput. Phys. Geonho Hwang, Yesom Park, Yueun Lee, Myungjoo Kang
2024	J	`jnl`	Expressive Power of ReLU and Step Networks under Floating-Point Operations. CoRR Yeachan Park, Geonho Hwang, Wonyeol Lee, Sejun Park
2024	J	`jnl`	Expressive power of ReLU and step networks under floating-point operations. Neural Networks Yeachan Park, Geonho Hwang, Wonyeol Lee, Sejun Park
2024	J	`jnl`	On Expressive Power of Quantized Neural Networks under Fixed-Point Arithmetic. CoRR Geonho Hwang, Yeachan Park, Sejun Park
2023	J	`jnl`	Disentangling the correlated continuous and discrete generative factors of data. Pattern Recognit. Jaewoong Choi, Geonho Hwang, Myungjoo Kang
2023	A*	`conf`	Finding the Global Semantic Representation in GAN through Fréchet Mean. ICLR Jaewoong Choi, Geonho Hwang, Hyunsoo Cho, Myungjoo Kang
2023	A*	`conf`	MAGANet: Achieving Combinatorial Generalization by Modeling a Group Action. ICML Geonho Hwang, Jaewoong Choi, Hyunsoo Cho, Myungjoo Kang
2023	J	`jnl`	Minimal Width for Universal Property of Deep RNN. J. Mach. Learn. Res. Changhoon Song, Geonho Hwang, Junho Lee, Myungjoo Kang
2023	J	`jnl`	Minimum Width for Deep, Narrow MLP: A Diffeomorphism and the Whitney Embedding Theorem Approach. CoRR Geonho Hwang
2022	J	`jnl`	Analyzing the Latent Space of GAN through Local Dimension Estimation. CoRR Jaewoong Choi, Geonho Hwang, Hyunsoo Cho, Myungjoo Kang
2022	A*	`conf`	Do Not Escape From the Manifold: Discovering the Local Coordinates on the Latent Space of GANs. ICLR Jaewoong Choi, Junho Lee, Changyeon Yoon, Jung Ho Park, Geonho Hwang, Myungjoo Kang
2022	J	`jnl`	Finding the global semantic representation in GAN through Frechet Mean. CoRR Jaewoong Choi, Geonho Hwang, Hyunsoo Cho, Myungjoo Kang
2022	J	`jnl`	Minimal Width for Universal Property of Deep RNN. CoRR Changhoon Song, Geonho Hwang, Junho Lee, Myungjoo Kang
2022	J	`jnl`	Universal Property of Convolutional Neural Networks. CoRR Geonho Hwang, Myungjoo Kang
2021	J	`jnl`	Do Not Escape From the Manifold: Discovering the Local Coordinates on the Latent Space of GANs. CoRR Jaewoong Choi, Changyeon Yoon, Junho Lee, Jung Ho Park, Geonho Hwang, Myungjoo Kang
2020	J	`jnl`	Discond-VAE: Disentangling Continuous Factors from the Discrete. CoRR Jaewoong Choi, Geonho Hwang, Myungjoo Kang

Nicolò Altamura is a security engineer and computer science researcher specializing in software protection, reverse engineering, program analysis, and binary deobfuscation. He holds a master's degree in Computer Science and Engineering from the University of Verona, graduating with honors, and works at emproof, focusing on software protection and the challenges of analyzing and understanding compiled software.

His research explores the relationship between program analysis and code obfuscation, with particular emphasis on how software transformations affect the ability of automated tools to recover program behavior. His interests span static analysis, binary analysis, decompilation, reverse engineering, and techniques for analyzing and simplifying obfuscated code.

Beyond academic research, Nicolò develops and contributes to tools for reverse engineering and software analysis. His projects include obfuscation_analysis, a Binary Ninja plugin for analyzing and simplifying obfuscated code; Pocket, an obfuscator for mixed Boolean-arithmetic expressions; and ptxNinja, a tool for reverse engineering NVIDIA GPU code represented in the PTX virtual instruction set.

His research also extends to binary datasets and software evolution. In 2026, he co-authored ASSEMBLAGE-DEEPHISTORY: A Cross-Build Binary Dataset with Temporal Coverage, a research work addressing the availability of binary data across multiple software builds and points in time, supporting research into binary analysis and changes between program versions.

Across his research, engineering work, and open-source contributions, Nicolò focuses on advancing the understanding, analysis, and protection of software against increasingly sophisticated obfuscation and reverse-engineering challenges.

Nicolò Altamura is not a football player. Please avoid reporting this kind of infromation.