Daniel E. Worrall

35 papers A* 9A 1B 1Journal 19Unranked 4

Year	Rank	Type	Title / Venue / Authors
2023	A	`conf`	Neural Simulated Annealing. AISTATS Alvaro H. C. Correia, Daniel E. Worrall, Roberto Bondesan
2022	B	`conf`	Learning Perturbations for Soft-Output Linear MIMO Demappers. GLOBECOM Daniel E. Worrall, Markus Peschl, Arash Behboodi, Roberto Bondesan
2022	J	`jnl`	Learning Perturbations for Soft-Output Linear MIMO Demappers. CoRR Daniel E. Worrall, Markus Peschl, Arash Behboodi, Roberto Bondesan
2022	A*	`conf`	Lie Point Symmetry Data Augmentation for Neural PDE Solvers. ICML Johannes Brandstetter, Max Welling, Daniel E. Worrall
2022	J	`jnl`	Lie Point Symmetry Data Augmentation for Neural PDE Solvers. CoRR Johannes Brandstetter, Max Welling, Daniel E. Worrall
2022	A*	`conf`	Message Passing Neural PDE Solvers. ICLR Johannes Brandstetter, Daniel E. Worrall, Max Welling
2022	J	`jnl`	Message Passing Neural PDE Solvers. CoRR Johannes Brandstetter, Daniel E. Worrall, Max Welling
2022	J	`jnl`	Neural Simulated Annealing. CoRR Alvaro H. C. Correia, Daniel E. Worrall, Roberto Bondesan
2021	J	`jnl`	Uncertainty modelling in deep learning for safer neuroimage enhancement: Demonstration in diffusion MRI. NeuroImage Ryutaro Tanno, Daniel E. Worrall, Enrico Kaden, Aurobrata Ghosh, Francesco Grussu, Alberto Bizzi, Stamatios N. Sotiropoulos, Antonio Criminisi, Daniel C. Alexander
2020	A*	`conf`	MDP Homomorphic Networks: Group Symmetries in Reinforcement Learning. NeurIPS Elise van der Pol, Daniel E. Worrall, Herke van Hoof, Frans A. Oliehoek, Max Welling
2020	J	`jnl`	MDP Homomorphic Networks: Group Symmetries in Reinforcement Learning. CoRR Elise van der Pol, Daniel E. Worrall, Herke van Hoof, Frans A. Oliehoek, Max Welling
2020	A*	`conf`	SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks. NeurIPS Fabian Fuchs, Daniel E. Worrall, Volker Fischer, Max Welling
2020	J	`jnl`	SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks. CoRR Fabian B. Fuchs, Daniel E. Worrall, Volker Fischer, Max Welling
2019	J	`jnl`	Affine Self Convolution. CoRR Nichita Diaconu, Daniel E. Worrall
2019	A*	`conf`	Deep Scale-spaces: Equivariance Over Scale. NeurIPS Daniel E. Worrall, Max Welling
2019	J	`jnl`	Deep Scale-spaces: Equivariance Over Scale. CoRR Daniel E. Worrall, Max Welling
2019	—	`—`	Equivariance for deep learning and retinal imaging. Daniel E. Worrall
2019	J	`jnl`	Learning Likelihoods with Conditional Normalizing Flows. CoRR Christina Winkler, Daniel E. Worrall, Emiel Hoogeboom, Max Welling
2019	A*	`conf`	Learning to Convolve: A Generalized Weight-Tying Approach. ICML Nichita Diaconu, Daniel E. Worrall
2019	J	`jnl`	Learning to Convolve: A Generalized Weight-Tying Approach. CoRR Nichita Diaconu, Daniel E. Worrall
2019	A*	`conf`	Reversible GANs for Memory-Efficient Image-To-Image Translation. CVPR Tycho F. A. van der Ouderaa, Daniel E. Worrall
2019	J	`jnl`	Reversible GANs for Memory-efficient Image-to-Image Translation. CoRR Tycho F. A. van der Ouderaa, Daniel E. Worrall
2019	—	`conf`	Supervised Uncertainty Quantification for Segmentation with Multiple Annotations. MICCAI (2) Shi Hu, Daniel E. Worrall, Stefan Knegt, Bas Veeling, Henkjan J. Huisman, Max Welling
2019	J	`jnl`	Supervised Uncertainty Quantification for Segmentation with Multiple Annotations. CoRR Shi Hu, Daniel E. Worrall, Stefan Knegt, Bastiaan S. Veeling, Henkjan J. Huisman, Max Welling
2019	J	`jnl`	Uncertainty Quantification in Deep Learning for Safer Neuroimage Enhancement. CoRR Ryutaro Tanno, Daniel E. Worrall, Enrico Kaden, Aurobrata Ghosh, Francesco Grussu, Alberto Bizzi, Stamatios N. Sotiropoulos, Antonio Criminisi, Daniel C. Alexander
2018	—	`conf`	CubeNet: Equivariance to 3D Rotation and Translation. ECCV (5) Daniel E. Worrall, Gabriel J. Brostow
2018	J	`jnl`	CubeNet: Equivariance to 3D Rotation and Translation. CoRR Daniel E. Worrall, Gabriel J. Brostow
2017	—	`conf`	Bayesian Image Quality Transfer with CNNs: Exploring Uncertainty in dMRI Super-Resolution. MICCAI (1) Ryutaro Tanno, Daniel E. Worrall, Aurobrata Ghosh, Enrico Kaden, Stamatios N. Sotiropoulos, Antonio Criminisi, Daniel C. Alexander
2017	J	`jnl`	Bayesian Image Quality Transfer with CNNs: Exploring Uncertainty in dMRI Super-Resolution. CoRR Ryutaro Tanno, Daniel E. Worrall, Aurobrata Ghosh, Enrico Kaden, Stamatios N. Sotiropoulos, Antonio Criminisi, Daniel C. Alexander
2017	A*	`conf`	Harmonic Networks: Deep Translation and Rotation Equivariance. CVPR Daniel E. Worrall, Stephan J. Garbin, Daniyar Turmukhambetov, Gabriel J. Brostow
2017	A*	`conf`	Interpretable Transformations with Encoder-Decoder Networks. ICCV Daniel E. Worrall, Stephan J. Garbin, Daniyar Turmukhambetov, Gabriel J. Brostow
2017	J	`jnl`	Interpretable Transformations with Encoder-Decoder Networks. CoRR Daniel E. Worrall, Stephan J. Garbin, Daniyar Turmukhambetov, Gabriel J. Brostow
2017	J	`jnl`	Virtual Adversarial Ladder Networks For Semi-supervised Learning. CoRR Saki Shinoda, Daniel E. Worrall, Gabriel J. Brostow
2016	—	`conf`	Automated Retinopathy of Prematurity Case Detection with Convolutional Neural Networks. LABELS/DLMIA@MICCAI Daniel E. Worrall, Clare M. Wilson, Gabriel J. Brostow
2016	J	`jnl`	Harmonic Networks: Deep Translation and Rotation Equivariance. CoRR Daniel E. Worrall, Stephan J. Garbin, Daniyar Turmukhambetov, Gabriel J. Brostow

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.