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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.

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.

pybhc

Bayesian Hierarchical clustering in python

pyslds

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.

semeval-2017-task-4-a-b-c-using-bert

ssm

Bayesian learning and inference for state space models

st-fmri

This repository contains code for spatio-temporal deep learning on functional MRI data

stance-semeval2016

USFD submission code for Semeval 2016 Task 6, Subtask B

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.