Jiayi Chen's Projects
A small dataset for generating art image from a real-world object (represented by multiple views of 3D model)
A beautiful, simple, clean, and responsive Jekyll theme for academics
AutoML
2023 up-to-date list of DATASETS, CODEBASES and PAPERS on Multi-Task Learning (MTL), from Machine Learning perspective.
Reading list for research topics in multimodal machine learning
Python implementation for Reinforcement Learning algorithms -- Bandit algorithms, MDP, Dynamic Programming (value/policy iteration), Model-free Control (off-policy Monte Carlo, Q-learning)
A toy project for affective text generation via Controllable Sequence Generative Adversarial Network .
A Research-oriented Federated Learning Library and Benchmark Platform for Graph Neural Networks. Accepted to ICLR'2021 - DPML and MLSys'21 - GNNSys workshops.
The purpose of this repo is to make it easy to get started with JAX, Flax, and Haiku. It contains my "Machine Learning with JAX" series of tutorials (YouTube videos and Jupyter Notebooks) as well as the content I found useful while learning about the JAX ecosystem.
Mutation tool for graph neural networks
Illumination-guided Neural Style Transfer is intended for two tasks: Neural Style Transfer, and Painterly Rendering 3d Model
Build a Jekyll blog in minutes, without touching the command line.
A PyTorch Library for Multi-Task Learning
Multimodal datasets.
This repository contains various models targetting multimodal representation learning, multimodal fusion for downstream tasks such as multimodal sentiment analysis.
Implementation of Bag of Words, Perceptron & Averaged Perceptron, Logistic Regression, POS Tagging, Hidden Markov Model, LSTM Language Model
Painterly Rendering 3d Model based on PatchMatch algorithm
Collection of generative models in Pytorch version.
Object detection in images, and tracking across video frames
Python Implementation of algorithms in Social Media Mining, e.g., Recommendation, Collaborative Filtering, Community Detection, Spectral Clustering, Modularity Maximization, co-authorship networks.
EM algorithms, linear regression, markov chain
Deep learning-based tissue compositions and cell-type-specific gene expression analysis with tissue-adaptive autoencoder (TAPE)
🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.