110 Open source projects that are alternatives of or similar to tulip

A Closer Look at Accuracy vs. Robustness

A curated list of awesome resources regarding machine learning reliability.

Task-agnostic universal black-box attacks on computer vision neural network via procedural noise (CCS'19)

Adversarial Robustness Toolbox (ART) - Python Library for Machine Learning Security - Evasion, Poisoning, Extraction, Inference - Red and Blue Teams

The goal of this survey is two-fold: (i) to present recent advances on adversarial machine learning (AML) for the security of RS (i.e., attacking and defense recommendation models), (ii) to show another successful application of AML in generative adversarial networks (GANs) for generative applications, thanks to their ability for learning (high-…

Provably defending pretrained classifiers including the Azure, Google, AWS, and Clarifai APIs

Efficient Algorithms for L0 Regularized Learning

Code for the unrestricted adversarial examples paper (NeurIPS 2018)

Feature Scattering Adversarial Training

Attacking Vision based Perception in End-to-end Autonomous Driving Models

Hyperstar: Negative Sampling Improves Hypernymy Extraction Based on Projection Learning.

Transferable End-to-End Aspect-based Sentiment Analysis with Selective Adversarial Learning (EMNLP'19)

Code to reproduce the attacks and defenses for the entries "JeromeR" in the NIPS 2018 Adversarial Vision Challenge

SHIELD: Fast, Practical Defense and Vaccination for Deep Learning using JPEG Compression

Collateral Learning - Functional Encryption and Adversarial Training on partially encrypted networks

Robustness benchmark for DNN models.

This repository contains a new adversarial training method for Generative Conversational Agents

Software for learning sparse Bayesian networks

Reimplementing cool papers in PyTorch...

My entry for ICLR 2018 Reproducibility Challenge for paper Synthesizing robust adversarial examples https://openreview.net/pdf?id=BJDH5M-AW

Pre-Training Buys Better Robustness and Uncertainty Estimates (ICML 2019)

Implemention of Fast Gradient Sign Method for generating adversarial examples in Keras

EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examples

Code implementation of the paper "With Great Training Comes Great Vulnerability: Practical Attacks against Transfer Learning", at USENIX Security 2018

Code for the paper: Adversarial Training Against Location-Optimized Adversarial Patches. ECCV-W 2020.

Statistical Models with Regularization in Pure Julia

A document covering machine learning basics. 🤖📊

Implementation of "Disentangled Representation Learning for Non-Parallel Text Style Transfer(ACL 2019)" in Pytorch

Athena: A Framework for Defending Machine Learning Systems Against Adversarial Attacks

Adversarial Attacks on Deep Neural Networks for Time Series Classification

Randomized Smoothing of All Shapes and Sizes (ICML 2020).

Tensorflow Implementation of open set domain adaptation by backpropagation

Official implementation codes of "Regularizing neural networks for future trajectory prediction via IRL framework"

Neural Network Distiller by Intel AI Lab: a Python package for neural network compression research. https://intellabs.github.io/distiller

AISTATS 2019: Reference-based Adversarial Sampling & Its applications to Soft Q-learning

Implementation of key concepts of neuralnetwork via numpy

Iterative unfolding for Python

Machine Learning models from scratch with a better visualisation

Ordered Weighted L1 regularization for classification and regression in Python

adversarial examples, adversarial malware examples, adversarial malware detection, adversarial deep ensemble, Android malware variants

This is a Statistical Learning application which will consist of various Machine Learning algorithms and their implementation in R done by me and their in depth interpretation.Documents and reports related to the below mentioned techniques can be found on my Rpubs profile.

机器学习-Coursera-吴恩达- python+Matlab代码实现

Multimodal Adversarial Network for Cross-modal Retrieval (PyTorch Code)

Implementation of models in our EMNLP 2019 paper: A Logic-Driven Framework for Consistency of Neural Models

Code for our CVPR 2018 paper, "On the Robustness of Semantic Segmentation Models to Adversarial Attacks"

Deep Learning Specialization Course by Coursera. Neural Networks, Deep Learning, Hyper Tuning, Regularization, Optimization, Data Processing, Convolutional NN, Sequence Models are including this Course.

Code for our paper "Regularizing Neural Networks via Adversarial Model Perturbation", CVPR2021

Julia package of regularization functions for machine learning

[ICML 2019, 20 min long talk] Robust Decision Trees Against Adversarial Examples

Codes and Datasets for paper RecSys'20 "SSE-PT: Sequential Recommendation Via Personalized Transformer" and NurIPS'19 "Stochastic Shared Embeddings: Data-driven Regularization of Embedding Layers"

[ICLR'21] PyTorch code for our paper "Neural Pruning via Growing Regularization"

🧠👨‍💻Deep Learning Specialization • Lecture Notes • Lab Assignments

AI Learning Hub for Machine Learning, Deep Learning, Computer Vision and Statistics

Python for《Deep Learning》，该书为《深度学习》(花书) 数学推导、原理剖析与源码级别代码实现

Implementation of 'Self-Adversarial Variational Autoencoder with Gaussian Anomaly Prior Distribution for Anomaly Detection'

An implementation of "Fusing Structure and Content via Non-negative Matrix Factorization for Embedding Information Networks".

Tensorflow implementation of the Manifold Mixup machine learning research paper

speechpro.com/

PyTorch reimplementation of the paper: "Semantic Pyramid for Image Generation" [CVPR 2020].

[ICML 2021] A fast algorithm for fitting robust decision trees. http://proceedings.mlr.press/v139/vos21a.html