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Yizhe Zhang's Projects

a-nice-mc icon a-nice-mc

Code for "A-NICE-MC: Adversarial Training for MCMC"

cocon icon cocon

Consistent dialogue generation

code-act icon code-act

Official Repo for paper "Executable Code Actions Elicit Better LLM Agents" by Xingyao Wang, Yangyi Chen, Lifan Yuan, Yizhe Zhang, Yunzhu Li, Hao Peng, Heng Ji.

coupledem icon coupledem

Expectation maximization for coupled HMM-regression inference

dialogrpt icon dialogrpt

EMNLP 2020: "Dialogue Response Ranking Training with Large-Scale Human Feedback Data"

dreasysnail.github.io icon dreasysnail.github.io

Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes

get-started-with-jax icon get-started-with-jax

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.

gpt-3 icon gpt-3

GPT-3: Language Models are Few-Shot Learners

inset icon inset

INSET: Sentence Infilling with Inter-sentential Transformer

most icon most

MOST+ is a fast MOTIF finding tool(MOtif finding by Suffix tree and heterogeneous Tags). MOST+ is faster than existing motif finding algorithms to our knowledge by taking advantage of suffix tree. It also use heterogeneous information, like histone modification or DNase I sensitivity, to help detect motif, thus rendering a higher level of accuracy.

nlp-recipes icon nlp-recipes

Natural Language Processing Best Practices & Examples

ot-seq2seq icon ot-seq2seq

code for paper "Improving Sequence-to-Sequence Learning via Optimal Transport"

pytorch-pretrained-bert icon pytorch-pretrained-bert

πŸ“–The Big-&-Extending-Repository-of-Transformers: Pretrained PyTorch models for Google's BERT, OpenAI GPT & GPT-2, Google/CMU Transformer-XL.

sgmgt icon sgmgt

Implementation for the paper "Stochastic Gradient Monomial Gamma Sampler"

stylefusion icon stylefusion

code/data for "Structuring Latent Spaces for Stylized Response Generation" (Gao et al., EMNLP'19)

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