davidstap

AttnGAN

stylegan

StyleGAN - Official TensorFlow Implementation

encoder-decoder

Encoder-decoder model with attention (Luong), with two LSTM layers with 500 hidden units on both encoder and decoder side. The vocabulary size on both source (english) and target side (Dutch) is 50000. The model is trained on the train part of the TED dataset (https://wit3.fbk.eu/mt.php?release=2017-01-trnmted), maximum sequence length 50.

ML4QS

Machine Learning for the Quantified Self

celeba-hq-modified

Modified h5tool.py make user getting celeba-HQ easier

multilingual-kNN-mt

acl-anthology

Data and software for building the ACL Anthology.

americasnlp2023

Cross-Modal-Projection-Learning

TensorFlow Implementation of Deep Cross-Modal Projection Learning

davidstap.github.io

evolutionary-computing

fast-knn-nmt

InfoGAN-PyTorch

PyTorch Implementation of InfoGAN

IR1

Homeworks for Information Retrieval 1 (MSc Artificial Intelligence)

lafand-mt-srn

MAFAND-MT

leren

models

Models and examples built with TensorFlow

mosesdecoder

Moses, the machine translation system

mt-archive

Project for ACL. Converting all the data from http://www.mt-archive.info into the ACL standard XML format.

mteb

MTEB: Massive Text Embedding Benchmark

natural-instructions-expansion

Expanding natural instructions

nlp2

NLP2 course page

NLP2_NMT

Pytorch_InfoGAN

Easy InfoGAN Implementation in Pytorch

sat_ml_sudoku

Knowledge Representation project (MSc Artificial Intelligence): use ML to predict statistics of SAT solver