Liang Chen's Projects
Python implementation of algorithms from Russell And Norvig's "Artificial Intelligence - A Modern Approach"
The Additive Margin MobileNet1D is a new light weight deep learning model for Speaker Recognition which is based on the MobileNetV2 architecture and the Additive Margin Softmax (AM-Softmax) loss function.)
Organized inventory of research using the Abstract Meaning Representation
Code for our paper "Graph Pre-training for AMR Parsing and Generation" in ACL2022
A python library that makes AMR parsing, generation and visualization simple.
A simple prompt-chatting AI based on wechaty and fintuned NLP model
Source code for paper "ATP: AMRize Than Parse! Enhancing AMR Parsing with PseudoAMRs" @NAACL-2022
AutoVC: Zero-Shot Voice Style Transfer with Only Autoencoder Loss
:sparkles::sparkles:Latest Papers and Datasets on Multimodal Large Language Models, and Their Evaluation.
🐫 CAMEL: Communicative Agents for “Mind” Exploration of Large Scale Language Model Society
李宏毅GAN课程cGAN动漫人物头像生成实现代码(含训练数据)
Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes
计算语言学22-23学年秋季学期 课程大作业baseline实现
Code for "Semantic Role Labeling as Dependency Parsing: Exploring Latent Tree Structures Inside Arguments".
MMM 2021: Crossed-Time Delay Neural Network for Speaker Recognition
🤗 The largest hub of ready-to-use datasets for ML models with fast, easy-to-use and efficient data manipulation tools
Deep Speaker: an End-to-End Neural Speaker Embedding System.
DialogSum: A Real-life Scenario Dialogue Summarization Dataset - Findings of ACL 2021
Reproduce the result of different dialogue summarization models.
特征提取/数据降维:PCA、LDA、MDS、LLE、TSNE等降维算法的python实现
PyTorch implementation of "Learning to Discover Cross-Domain Relations with Generative Adversarial Networks"
Paper collections of methods that using language to interact with environment, including interact with real world, simulated world or WWW(🏄).
A set of examples around pytorch in Vision, Text, Reinforcement Learning, etc.
Code for paper: An Image is Worth 1/2 Tokens After Layer 2: Plug-and-Play Inference Acceleration for Large Vision-Language Models