LorenzoCazzaro
PhD student in Computer Science. Interested in Trustworthy Machine Learning, Cyber Security and applications of AI in Cyber Security
Ca' Foscari University of Venice - IT DepartmentVenice
LorenzoCazzaro's Stars
microsoft/PowerToys
Windows system utilities to maximize productivity
scikit-learn/scikit-learn
scikit-learn: machine learning in Python
radareorg/radare2
UNIX-like reverse engineering framework and command-line toolset
mytechnotalent/Reverse-Engineering
A FREE comprehensive reverse engineering tutorial covering x86, x64, 32-bit/64-bit ARM and embedded RISC-V architectures.
igorbarinov/awesome-data-engineering
A curated list of data engineering tools for software developers
tensorforce/tensorforce
Tensorforce: a TensorFlow library for applied reinforcement learning
fastai/fastmac
Get a MacOS or Linux shell, for free, in around 2 minutes
Harry24k/adversarial-attacks-pytorch
PyTorch implementation of adversarial attacks [torchattacks]
standardebooks/tools
The Standard Ebooks toolset for producing our ebook files.
MadryLab/robustness
A library for experimenting with, training and evaluating neural networks, with a focus on adversarial robustness.
MadryLab/mnist_challenge
A challenge to explore adversarial robustness of neural networks on MNIST.
fra31/auto-attack
Code relative to "Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks"
thu-ml/ares
A Python library for adversarial machine learning focusing on benchmarking adversarial robustness.
HCDM/BanditLib
Library of contextual bandits algorithms
cristaloleg/awesome-manifesto
Utopian developer's manifesto
microsoft/coax
This project was moved to: https://github.com/coax-dev/coax
max-andr/square-attack
Square Attack: a query-efficient black-box adversarial attack via random search [ECCV 2020]
usnistgov/trojai-literature
akhadangi/Multi-armed-Bandits
In this notebook several classes of multi-armed bandits are implemented. This includes epsilon greedy, UCB, Linear UCB (Contextual bandits) and Kernel UCB. Some of the well cited papers in this context are also implemented.
Curt-Park/handwritten_digit_recognition
Handwritten digit recognition with MNIST & Keras
ftramer/MultiRobustness
Code for the paper "Adversarial Training and Robustness for Multiple Perturbations", NeurIPS 2019
ebtaleb/peda_cheatsheet
suyeecav/Hybrid-Attack
nsfzyzz/boundary_thickness
[NeurIPS 2020] code for "Boundary thickness and robustness in learning models"
kibercthulhu/gdb-peda-cheatsheet
GDBP-PEDA Cheat Sheet
veneres/unive-ms-thesis-template
Ca' Foscari University LaTeX master thesis template
GiBg1aN/Clustering_Library
Implementation of main clustering algorithms
riccardobernardi/Manifold-Learning-and-Graph-Kernels
LorenzoBoccalon/bioinformatics
Bioinformatics Course Algorithms.
riccardobernardi/Docs-Assignment1-TensorLib