rgbayrak's Stars
huggingface/transformers
🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
Lightning-AI/pytorch-lightning
Pretrain, finetune ANY AI model of ANY size on multiple GPUs, TPUs with zero code changes.
terryum/awesome-deep-learning-papers
The most cited deep learning papers
aleju/imgaug
Image augmentation for machine learning experiments.
voxel51/fiftyone
Refine high-quality datasets and visual AI models
PaddlePaddle/VisualDL
Deep Learning Visualization Toolkit（『飞桨』深度学习可视化工具 ）
Nixtla/statsforecast
Lightning ⚡️ fast forecasting with statistical and econometric models.
paperswithcode/releasing-research-code
Tips for releasing research code in Machine Learning (with official NeurIPS 2020 recommendations)
qingsongedu/time-series-transformers-review
A professionally curated list of awesome resources (paper, code, data, etc.) on transformers in time series.
cure-lab/LTSF-Linear
[AAAI-23 Oral] Official implementation of the paper "Are Transformers Effective for Time Series Forecasting?"
thuml/Autoformer
About Code release for "Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting" (NeurIPS 2021), https://arxiv.org/abs/2106.13008
neuropsychology/NeuroKit
NeuroKit2: The Python Toolbox for Neurophysiological Signal Processing
sgrvinod/Deep-Tutorials-for-PyTorch
In-depth tutorials for implementing deep learning models on your own with PyTorch.
time-series-foundation-models/lag-llama
Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting
MAZiqing/FEDformer
ThomasYeoLab/CBIG
leipzig/awesome-reproducible-research
A curated list of reproducible research case studies, projects, tutorials, and media
husnejahan/DeepAR-pytorch
canlab/CanlabCore
Core tools required for running Canlab Matlab toolboxes. The heart of this toolbox is object-oriented tools that enable interactive analysis of neuroimaging data and simple scripts using high-level commands tailored to neuroimaging analysis.
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.
JonathanCrabbe/Dynamask
This repository contains the implementation of Dynamask, a method to identify the features that are salient for a model to issue its prediction when the data is represented in terms of time series. For more details on the theoretical side, please read our ICML 2021 paper: 'Explaining Time Series Predictions with Dynamic Masks'.
vanderschaarlab/Interpretability
Resources for Machine Learning Explainability
qihongl/learn-hippo
Lu, Q., Hasson, U., & Norman, K. A. (2022). A neural network model of when to retrieve and encode episodic memories. eLife
adolphslab/HCP_MRI-behavior
Code for predicting individual differences in behavioral variables (e.g., intelligence, personality) from resting-state fMRI functional connectivity, using data from the Young Adult Human Connectome Project
arthurmensch/cogspaces
Multi-study decoding models for task functional MRI data.
ljchang/CosanlabToolbox
General Analysis Functions
asoroosh/xDF
xDF estimates variance of Pearson's correlations among highly autocorrelated time series.
neurodatascience/dFC
An implementation of several well-known dynamic Functional Connectivity assessment methods.
nerdslab/EIT
PyTorch implementation of "Seeing the forest and the tree: Building representations of both individual and collective dynamics with transformers" (NeurIPS 2022)
Scaleformer/Scaleformer
Scaleformer: Iterative Multi-scale Refining Transformers for Time Series Forecasting