Subati A's repositories
Attention-Based-Siamese-Text-CNN-for-Stance-Detection
Final project for NLP(DATA130006) in Fudan university.
autolfads-tf2
A TensorFlow 2.0 implementation of Latent Factor Analysis via Dynamical Systems (LFADS) and AutoLFADS.
disentangling-vae
Experiments for understanding disentanglement in VAE latent representations
dynamax
State Space Models library in JAX
dynamicnetworks
Interpret dynamic functional connectivity in brain imaging by comparing methods
expressive-latent-dynamics-paper
Code to reproduce experiments from Sedler, A, Versteeg, C, Pandarinath, C. "Expressive architectures enhance interpretability of dynamics-based neural population models". Neurons, Behavior, Data analysis, and Theory 2023.
Financial-Models-Numerical-Methods
Collection of notebooks about quantitative finance, with interactive python code.
gpt-2
Code for the paper "Language Models are Unsupervised Multitask Learners"
hbnm
Hierarchical brain network model (Demirtas et al., 2019)
intro_dgm
An Introduction to Deep Generative Modeling: Examples
latent_dynamics_workshop
Exercises and examples for the latent dynamics workshop
LatentDiffEq.jl
Latent Differential Equations models in Julia.
lfads-torch
A PyTorch implementation of Latent Factor Analysis via Dynamical Systems (LFADS) and AutoLFADS.
mdl-stance-robustness
Multi-dataset stance detection and robustness experiments
osl-dynamics
Methods for studying dynamic functional brain activity in neuroimaging data.
Pairs-Trading-as-application-to-the-Ornstein-Uhlenbeck-Process
A model simulation shows how pairs trading could be used for two S&P500 traded stocks. It proofs that the strategy is successful on real data.
pyBHC
Bayesian Hierarchical clustering in python
recurrent-slds
Recurrent Switching Linear Dynamical Systems
rmsd
Calculate Root-mean-square deviation (RMSD) of two molecules, using rotation, in xyz or pdb format
rsfMRI-VAE
Pytorch implementation of 'Representation Learning of Resting State fMRI with Variational Autoencoder'
SE16-Task6-Stance-Detection
Code for SemEval-16 Task6 subtaskA and subtaskB.
ssm
Bayesian learning and inference for state space models
ST-fMRI
This repository contains code for spatio-temporal deep learning on functional MRI data
stats320
STATS320: Statistical Methods for Neural Data Analysis
stocksight
Stock market analyzer and predictor using Elasticsearch, Twitter, News headlines and Python natural language processing and sentiment analysis
Taghia_Cai_NatureComm_2018
*Taghia J., *Cai W., Ryali S., Kochalka J., Nicholas J., Chen T., Menon V. (2018). Uncovering hidden brain state dynamics that regulate performance and decision-making during cognition. Nature Communications, 9(1):2505.
Taghia_Neuroimage_2017
Taghia, J., Ryali, S., Chen, T., Supekar, K., Cai, W., Menon, V. (2017). Bayesian Switching Factor Analysis for Estimating Time-varying Functional Connectivity in fMRI. NeuroImage, 155, pp. 271-290.