Daniel Becking

14 papers B 3Journal 7Unranked 4

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Efficient Federated Learning Tiny Language Models for Mobile Network Feature Prediction. CoRR Daniel Becking, Ingo Friese, Karsten Müller, Thomas Buchholz, Mandy Galkow-Schneider, Wojciech Samek, Detlev Marpe
2025	B	`conf`	Efficient Federated Learning of Mixed-Token Transformers for Cellular Feature Prediction. GLOBECOM Daniel Becking, Jost Arndt, Ingo Friese, Karsten Müller, Jackie Ma, Thomas Buchholz, Mandy Galkow-Schneider, Wojciech Samek, Detlev Marpe
2025	—	`conf`	Relevance-Guided Activation Sparsification for Bandwidth-Efficient Collaborative Inference. ICCVW Maximilian Andreas Hoefler, Daniel Becking, Karsten Müller, Detlev Marpe, Wojciech Samek
2024	J	`jnl`	Neural Network Coding of Difference Updates for Efficient Distributed Learning Communication. IEEE Trans. Multim. Daniel Becking, Karsten Müller, Paul Haase, Heiner Kirchhoffer, Gerhard Tech, Wojciech Samek, Heiko Schwarz, Detlev Marpe, Thomas Wiegand
2022	—	`conf`	Adaptive Differential Filters for Fast and Communication-Efficient Federated Learning. CVPR Workshops Daniel Becking, Heiner Kirchhoffer, Gerhard Tech, Paul Haase, Karsten Müller, Heiko Schwarz, Wojciech Samek
2022	J	`jnl`	Adaptive Differential Filters for Fast and Communication-Efficient Federated Learning. CoRR Daniel Becking, Heiner Kirchhoffer, Gerhard Tech, Paul Haase, Karsten Müller, Heiko Schwarz, Wojciech Samek
2022	B	`conf`	History Dependent Significance Coding for Incremental Neural Network Compression. ICIP Gerhard Tech, Paul Haase, Daniel Becking, Heiner Kirchhoffer, Karsten Müller, Jonathan Pfaff, Heiko Schwarz, Wojciech Samek, Detlev Marpe, Thomas Wiegand
2021	J	`jnl`	ECQ CoRR Daniel Becking, Maximilian Dreyer, Wojciech Samek, Karsten Müller, Sebastian Lapuschkin
2021	B	`conf`	Encoder Optimizations For The NNR Standard On Neural Network Compression. ICIP Paul Haase, Daniel Becking, Heiner Kirchhoffer, Karsten Müller, Heiko Schwarz, Wojciech Samek, Detlev Marpe, Thomas Wiegand
2021	J	`jnl`	FantastIC4: A Hardware-Software Co-Design Approach for Efficiently Running 4Bit-Compact Multilayer Perceptrons. IEEE Open J. Circuits Syst. Simon Wiedemann, Suhas Shivapakash, Daniel Becking, Pablo Wiedemann, Wojciech Samek, Friedel Gerfers, Thomas Wiegand
2020	—	`conf`	ECQ xxAI@ICML Daniel Becking, Maximilian Dreyer, Wojciech Samek, Karsten Müller, Sebastian Lapuschkin
2020	J	`jnl`	FantastIC4: A Hardware-Software Co-Design Approach for Efficiently Running 4bit-Compact Multilayer Perceptrons. CoRR Simon Wiedemann, Suhas Shivapakash, Pablo Wiedemann, Daniel Becking, Wojciech Samek, Friedel Gerfers, Thomas Wiegand
2020	—	`conf`	Learning Sparse & Ternary Neural Networks with Entropy-Constrained Trained Ternarization (EC2T). CVPR Workshops Arturo Marbán, Daniel Becking, Simon Wiedemann, Wojciech Samek
2020	J	`jnl`	Learning Sparse & Ternary Neural Networks with Entropy-Constrained Trained Ternarization (EC2T). CoRR Arturo Marbán, Daniel Becking, Simon Wiedemann, Wojciech Samek

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.