Yali Amit - RankMe

42 papers A* 1Journal 36Unranked 4

Year	Rank	Type	Title / Venue / Authors
2022	J	`jnl`	Biologically Plausible Training Mechanisms for Self-Supervised Learning in Deep Networks. Frontiers Comput. Neurosci. Mufeng Tang, Yibo Yang, Yali Amit
2022	J	`jnl`	Detection Selection Algorithm: A Likelihood based Optimization Method to Perform Post Processing for Object Detection. CoRR Angzhi Fan, Benjamin Ticknor, Yali Amit
2021	J	`jnl`	Biologically Plausible Training Mechanisms for Self-Supervised Learning in Deep Networks. CoRR Mufeng Tang, Yibo Yang, Yali Amit
2021	J	`jnl`	Do We Really Need to Learn Representations from In-domain Data for Outlier Detection? CoRR Zhisheng Xiao, Qing Yan, Yali Amit
2021	J	`jnl`	EBMs Trained with Maximum Likelihood are Generator Models Trained with a Self-adverserial Loss. CoRR Zhisheng Xiao, Qing Yan, Yali Amit
2020	J	`jnl`	Exponential Tilting of Generative Models: Improving Sample Quality by Training and Sampling from Latent Energy. CoRR Zhisheng Xiao, Qing Yan, Yali Amit
2020	A*	`conf`	Likelihood Regret: An Out-of-Distribution Detection Score For Variational Auto-encoder. NeurIPS Zhisheng Xiao, Qing Yan, Yali Amit
2020	J	`jnl`	Likelihood Regret: An Out-of-Distribution Detection Score For Variational Auto-encoder. CoRR Zhisheng Xiao, Qing Yan, Yali Amit
2019	J	`jnl`	A Method to Model Conditional Distributions with Normalizing Flows. CoRR Zhisheng Xiao, Qing Yan, Yali Amit
2019	J	`jnl`	Deep Learning With Asymmetric Connections and Hebbian Updates. Frontiers Comput. Neurosci. Yali Amit
2019	J	`jnl`	Generative Latent Flow: A Framework for Non-adversarial Image Generation. CoRR Zhisheng Xiao, Qing Yan, Yi-An Chen, Yali Amit
2018	J	`jnl`	Biologically plausible deep learning. CoRR Yali Amit
2017	J	`jnl`	Deformable Classifiers. CoRR Jiajun Shen, Yali Amit
2017	J	`jnl`	Detecting Curved Edges in Noisy Images in Sublinear Time. J. Math. Imaging Vis. Yi-Qing Wang, Alain Trouvé, Yali Amit, Boaz Nadler
2017	J	`jnl`	Dynamic Partition Models. CoRR Marc Goessling, Yali Amit
2015	J	`jnl`	A Generative Statistical Algorithm for Automatic Detection of Complex Postures. PLoS Comput. Biol. Stanislav Nagy, Marc Goessling, Yali Amit, David Biron
2015	—	`conf`	Compact Part-Based Image Representations: Extremal Competition and Overgeneralization. ICLR (Workshop) Marc Goessling, Yali Amit
2015	J	`jnl`	Sparse Autoregressive Networks. CoRR Marc Goessling, Yali Amit
2014	J	`jnl`	Memory Capacity of Networks with Stochastic Binary Synapses. PLoS Comput. Biol. Alexis M. Dubreuil, Yali Amit, Nicolas Brunel
2014	—	`ch.`	Object Detection. Computer Vision, A Reference Guide Yali Amit, Pedro F. Felzenszwalb
2012	J	`jnl`	Recurrent network of perceptrons with three state synapses achieves competitive classification on real inputs. Frontiers Comput. Neurosci. Yali Amit, Jacob Walker
2011	J	`jnl`	Capacity analysis in multi-state synaptic models: a retrieval probability perspective. J. Comput. Neurosci. Yibi Huang, Yali Amit
2010	J	`jnl`	Precise Capacity Analysis in Binary Networks with Multiple Coding Level Inputs. Neural Comput. Yali Amit, Yibi Huang
2008	J	`jnl`	Efficient Annotation of Vesicle Dynamics Video Microscopy. IEEE Trans. Pattern Anal. Mach. Intell. Leandro Cortes, Yali Amit
2008	J	`jnl`	Optimizing One-Shot Learning with Binary Synapses. Neural Comput. Sandro Romani, Daniel J. Amit, Yali Amit
2007	J	`jnl`	POP: Patchwork of Parts Models for Object Recognition. Int. J. Comput. Vis. Yali Amit, Alain Trouvé
2006	—	`conf`	Generative Model and Consistent Estimation Algorithms for Non-Rigid Deformable Models. ICASSP (5) Stéphanie Allassonnière, Estelle Kuhn, Alain Trouvé, Yali Amit
2006	—	`conf`	Generative Models for Labeling Multi-object Configurations in Images. Toward Category-Level Object Recognition Yali Amit, Alain Trouvé
2005	—	`conf`	Part-Based Statistical Models for Object Classification and Detection. CVPR (2) Elliot Joel Bernstein, Yali Amit
2004	J	`jnl`	A Coarse-to-Fine Strategy for Multiclass Shape Detection. IEEE Trans. Pattern Anal. Mach. Intell. Yali Amit, Donald Geman, Xiaodong Fan
2001	J	`jnl`	Attractor Networks for Shape Recognition. Neural Comput. Yali Amit, Massimo Mascaro
2001	J	`jnl`	Speech recognition using randomized relational decision trees. IEEE Trans. Speech Audio Process. Yali Amit, Alejandro Murua
2000	J	`jnl`	A Neural Network Architecture for Visual Selection. Neural Comput. Yali Amit
1999	J	`jnl`	A Computational Model for Visual Selection. Neural Comput. Yali Amit, Donald Geman
1997	J	`jnl`	Graphical Shape Templates for Automatic Anatomy Detection with Applications to MRI Brain Scans. IEEE Trans. Medical Imaging Yali Amit
1997	J	`jnl`	Joint Induction of Shape Features and Tree Classifiers. IEEE Trans. Pattern Anal. Mach. Intell. Yali Amit, Kenneth Wilder
1997	J	`jnl`	Shape Quantization And Recognition With Randomized Trees. Neural Comput. Yali Amit, Donald Geman
1996	J	`jnl`	Graphical Templates for Model Registration. IEEE Trans. Pattern Anal. Mach. Intell. Yali Amit, Augustine Kong
1994	J	`jnl`	A Nonlinear Variational Problem for Image Matching. SIAM J. Sci. Comput. Yali Amit
1993	J	`jnl`	Deformable template models for emission tomography. IEEE Trans. Medical Imaging Yali Amit, Kevin M. Manbeck
1993	J	`jnl`	Large deviations for coding Markov chains and Gibbs random fields. IEEE Trans. Inf. Theory Yali Amit, Michael I. Miller
1992	J	`jnl`	Large deviations for the asymptotics of Ziv-Lempel codes for 2-D Gibbs fields. IEEE Trans. Inf. Theory Yali Amit, Michael I. Miller

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.