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Badr AlKhamissi's Projects

hf-cyclegan icon hf-cyclegan

Keras Implementation of Image Disentanglement and Uncooperative Re-Entanglement for High-Fidelity Image-to-Image Translation

keras-gan icon keras-gan

Keras implementations of Generative Adversarial Networks.

kinect-drawing icon kinect-drawing

Simple use of the Kinect sensor with Processing. Check README for video link!

language icon language

Evaluate computational models on their alignment to behavioral and neural measurements in the domain of language

lossmnist icon lossmnist

Exploring the discriminating power of different loss functions for classification

med icon med

Monotonicity Entailment Dataset (MED)

meta-rl-harlow icon meta-rl-harlow

PyTorch implementation of two variants of the Harlow visual fixation task (PsychLab and 1D version). Reproduces the results found in two papers. Check the ReadMe for more details!

meta-rl-twostep-task icon meta-rl-twostep-task

PyTorch implementation of Episodic Meta Reinforcement Learning on variants of the "Two-Step" task. Reproduces the results found in three papers. Check the ReadMe for more details!

multilingual-model-transfer icon multilingual-model-transfer

In this project we develop new deep learning models for bootstrapping language understanding models for languages with no labeled data using labeled data from other languages.

netpy icon netpy

Neural Networks library implementation using Numpy

photogenerator icon photogenerator

Generating a collage of a selected image using small number of selected images

rl-playground icon rl-playground

PyTorch RL Algorithms Implementations on OpenAI Gym Environments

sketchback icon sketchback

Keras implementation of sketch inversion using deep convolution neural networks (synthesising photo-realistic images from pencil sketches)

snake icon snake

Snake-حنش Re-imagined, using ProcessingJS

stories icon stories

Ruby on Rails Web App; People can contribute with each other to make up a story

switch-cuda icon switch-cuda

A simple bash script for switching between installed versions of CUDA.

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