Pinned Repositories
bert
TensorFlow code and pre-trained models for BERT
codebook
Convert source code into a pdf book using pandoc
copyAnonymousGithubRepo
A small tool to clone all files of a repository in anonymous.4open.science
evidential-deep-learning
Learn fast, scalable, and calibrated measures of uncertainty using neural networks!
examples
A set of examples around pytorch in Vision, Text, Reinforcement Learning, etc.
gpt-3
GPT-3: Language Models are Few-Shot Learners
Graph-MLPMixer
Repository for Graph MLP-Mixer
LLaVA
[NeurIPS'23 Oral] Visual Instruction Tuning: LLaVA (Large Language-and-Vision Assistant) built towards GPT-4V level capabilities.
net-serial-console-rs
paperplot
Simple setup utility for publication ready matplotlib figures
lihongchun2007's Repositories
lihongchun2007/copyAnonymousGithubRepo
A small tool to clone all files of a repository in anonymous.4open.science
lihongchun2007/bert
TensorFlow code and pre-trained models for BERT
lihongchun2007/codebook
Convert source code into a pdf book using pandoc
lihongchun2007/evidential-deep-learning
Learn fast, scalable, and calibrated measures of uncertainty using neural networks!
lihongchun2007/examples
A set of examples around pytorch in Vision, Text, Reinforcement Learning, etc.
lihongchun2007/gpt-3
GPT-3: Language Models are Few-Shot Learners
lihongchun2007/Graph-MLPMixer
Repository for Graph MLP-Mixer
lihongchun2007/LLaVA
[NeurIPS'23 Oral] Visual Instruction Tuning: LLaVA (Large Language-and-Vision Assistant) built towards GPT-4V level capabilities.
lihongchun2007/net-serial-console-rs
lihongchun2007/paperplot
Simple setup utility for publication ready matplotlib figures
lihongchun2007/Point-MAE
[ECCV2022] Masked Autoencoders for Point Cloud Self-supervised Learning
lihongchun2007/pvcnn
[NeurIPS 2019, Spotlight] Point-Voxel CNN for Efficient 3D Deep Learning
lihongchun2007/SciencePlots
Matplotlib styles for scientific plotting
lihongchun2007/segment-anything
The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.