Pinned 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.
DeepGenerativeModels
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)
SubatA20's Repositories
SubatA20/Attention-Based-Siamese-Text-CNN-for-Stance-Detection
Final project for NLP(DATA130006) in Fudan university.
SubatA20/autolfads-tf2
A TensorFlow 2.0 implementation of Latent Factor Analysis via Dynamical Systems (LFADS) and AutoLFADS.
SubatA20/DeepGenerativeModels
SubatA20/disentangling-vae
Experiments for understanding disentanglement in VAE latent representations
SubatA20/dynamax
State Space Models library in JAX
SubatA20/dynamicnetworks
Interpret dynamic functional connectivity in brain imaging by comparing methods
SubatA20/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.
SubatA20/Financial-Models-Numerical-Methods
Collection of notebooks about quantitative finance, with interactive python code.
SubatA20/gpt-2
Code for the paper "Language Models are Unsupervised Multitask Learners"
SubatA20/hbnm
Hierarchical brain network model (Demirtas et al., 2019)
SubatA20/intro_dgm
An Introduction to Deep Generative Modeling: Examples
SubatA20/latent_dynamics_workshop
Exercises and examples for the latent dynamics workshop
SubatA20/LatentDiffEq.jl
Latent Differential Equations models in Julia.
SubatA20/lfads-torch
A PyTorch implementation of Latent Factor Analysis via Dynamical Systems (LFADS) and AutoLFADS.
SubatA20/mdl-stance-robustness
Multi-dataset stance detection and robustness experiments
SubatA20/osl-dynamics
Methods for studying dynamic functional brain activity in neuroimaging data.
SubatA20/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.
SubatA20/pyBHC
Bayesian Hierarchical clustering in python
SubatA20/pyslds
SubatA20/recurrent-slds
Recurrent Switching Linear Dynamical Systems
SubatA20/rmsd
Calculate Root-mean-square deviation (RMSD) of two molecules, using rotation, in xyz or pdb format
SubatA20/rsfMRI-VAE
Pytorch implementation of 'Representation Learning of Resting State fMRI with Variational Autoencoder'
SubatA20/SemEval-2017-Task-4-A-B-C-using-BERT
SubatA20/ssm
Bayesian learning and inference for state space models
SubatA20/ST-fMRI
This repository contains code for spatio-temporal deep learning on functional MRI data
SubatA20/stats320
STATS320: Statistical Methods for Neural Data Analysis
SubatA20/stocksight
Stock market analyzer and predictor using Elasticsearch, Twitter, News headlines and Python natural language processing and sentiment analysis
SubatA20/strategies
quantitative trading with Javascript, Python, C++, PineScript, Blockly, MyLanguage(麦语言)
SubatA20/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.
SubatA20/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.