Yeganehfrh

Yeganehfrh's Stars

• ML4ITS/TimeVQVAE

  [official] PyTorch implementation of TimeVQVAE from the paper ["Vector Quantized Time Series Generation with a Bidirectional Prior Model", AISTATS 2023]

  Language:Jupyter Notebook10314

• abudesai/timeVAE

  TimeVAE implementation in keras/tensorflow

  Language:Python11123

• labmlai/annotated_deep_learning_paper_implementations

  🧑‍🏫 60+ Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠

  Language:Python54.4k5.6k

• amazon-science/chronos-forecasting

  Chronos: Pretrained (Language) Models for Probabilistic Time Series Forecasting

  Language:Python2.4k273

• YihaoAng/TSGBench

  TSGBench: Time Series Generation Benchmark (VLDB'24)

  Language:Python283

• jsyoon0823/TimeGAN

  Codebase for Time-series Generative Adversarial Networks (TimeGAN) - NeurIPS 2019

  Language:Jupyter Notebook841263

• morteza/MultiModalRest

  This code is outdated, see https://github.com/morteza/ACNets/tree/multimodal/ for an updated version.

  Language:Jupyter Notebook1

• kahartma/eeggan

  Language:Python54

• eriklindernoren/Keras-GAN

  Keras implementations of Generative Adversarial Networks.

  Language:Python9.2k3.1k

• keras-team/keras-core

  A multi-backend implementation of the Keras API, with support for TensorFlow, JAX, and PyTorch.

  Language:Python1.3k115

• facebookresearch/deepmeg-recurrent-encoder

  deepmeg recurrent encoder

  Language:Python246

• facebookresearch/brainmagick

  Training and evaluation pipeline for MEG and EEG brain signal encoding and decoding using deep learning. Code for our paper "Decoding speech perception from non-invasive brain recordings" published in Nature Machine Intelligence, 2023.

  Language:Python39659

• morteza/CogText

  Linking Theories and Methods in Cognitive Sciences via Joint Embedding of the Scientific Literature: The Example of Cognitive Control

  Language:Jupyter Notebook2

• athms/learning-from-brains

  Self-supervised learning techniques for neuroimaging data inspired by prominent learning frameworks in natural language processing + One of the broadest neuroimaging datasets used for pre-training to date.

  Language:Python7917

• fchollet/deep-learning-with-python-notebooks

  Jupyter notebooks for the code samples of the book "Deep Learning with Python"

  Language:Jupyter Notebook18.5k8.6k

• facebookresearch/fairseq

  Facebook AI Research Sequence-to-Sequence Toolkit written in Python.

  Language:Python30.2k6.4k

• shap/shap

  A game theoretic approach to explain the output of any machine learning model.

  Language:Jupyter Notebook22.6k3.3k

• fooof-tools/fooof

  Parameterizing neural power spectra into periodic & aperiodic components.

  Language:Python35897

• sappelhoff/pyprep

  PyPREP: A Python implementation of the Preprocessing Pipeline (PREP) for EEG data

  Language:Python13433

• robertoostenveld/bids-tools

  Tools for dealing with neuroimaging data in the BIDS structure

  Language:Python267