AdrienBenamira

AdriBena's starred repositories

MAD-GANs

Applied generative adversarial networks (GANs) to do anomaly detection for time series data

Language:Python50100

GAN-AD

We used generative adversarial networks (GANs) to do anomaly detection for time series data.

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Hardware_Performance_Counters_Can_Detect_Malware_Myth_or_Fact

Code and Data for AisaCCS 2018 paper: Hardware Performance Counters Can Detect Malware: Myth or Fact?

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VAT-pytorch

Virtual Adversarial Training (VAT) implementation for PyTorch

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art-DCGAN

Modified implementation of DCGAN focused on generative art. Includes pre-trained models for landscapes, nude-portraits, and others.

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yellowbrick

Visual analysis and diagnostic tools to facilitate machine learning model selection.

Language:PythonApache-2.0421900

This dataset contains 108,463 human-labeled and 656k noisily labeled pairs that feature the importance of modeling structure, context, and word order information for the problem of paraphrase identification.

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mathematics_dataset

This dataset code generates mathematical question and answer pairs, from a range of question types at roughly school-level difficulty.

Language:PythonApache-2.0174100

horovod

Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet.

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hyperband

Tuning hyperparams fast with Hyperband

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autodl_starting_kit_stable

Starting kit for AutoCV/AutoDL challenge (https://autodl.chalearn.org)

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openssl

TLS/SSL and crypto library

Language:CApache-2.02449000

SMoTherSpectre

Proof-of-concept code for the SMoTherSpectre exploit.

Language:C7100

sgx-step

A practical attack framework for precise enclave execution control

Language:CGPL-3.042900

stockpredictionai

In this noteboook I will create a complete process for predicting stock price movements. Follow along and we will achieve some pretty good results. For that purpose we will use a Generative Adversarial Network (GAN) with LSTM, a type of Recurrent Neural Network, as generator, and a Convolutional Neural Network, CNN, as a discriminator. We use LSTM for the obvious reason that we are trying to predict time series data. Why we use GAN and specifically CNN as a discriminator? That is a good question: there are special sections on that later.

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