jialin-yu's Stars
jacobgil/pytorch-grad-cam
Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.
jianghaojun/Awesome-Parameter-Efficient-Transfer-Learning
A collection of parameter-efficient transfer learning papers focusing on computer vision and multimodal domains.
tatsu-lab/stanford_alpaca
Code and documentation to train Stanford's Alpaca models, and generate the data.
arogozhnikov/einops
Flexible and powerful tensor operations for readable and reliable code (for pytorch, jax, TF and others)
fastai/fastai
The fastai deep learning library
scikit-image/scikit-image
Image processing in Python
mmistakes/minimal-mistakes
:triangular_ruler: Jekyll theme for building a personal site, blog, project documentation, or portfolio.
google/BIG-bench
Beyond the Imitation Game collaborative benchmark for measuring and extrapolating the capabilities of language models
yfzhang114/Generalization-Causality
关于domain generalization,domain adaptation,causality,robutness,prompt,optimization,generative model各式各样研究的阅读笔记
uber/causalml
Uplift modeling and causal inference with machine learning algorithms
gradio-app/gradio
Build and share delightful machine learning apps, all in Python. 🌟 Star to support our work!
recommenders-team/recommenders
Best Practices on Recommendation Systems
TransformerLensOrg/TransformerLens
A library for mechanistic interpretability of GPT-style language models
zhijing-jin/nlp-phd-global-equality
A repo for open resources & information for people to succeed in PhD in CS & career in AI / NLP
TheAlgorithms/Python
All Algorithms implemented in Python
junkunyuan/Awesome-Domain-Generalization
Awesome things about domain generalization, including papers, code, etc.
facebookresearch/DomainBed
DomainBed is a suite to test domain generalization algorithms
pgmpy/pgmpy
Python Library for learning (Structure and Parameter), inference (Probabilistic and Causal), and simulations in Bayesian Networks.
py-why/EconML
ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.
aesara-devs/aesara
Aesara is a Python library for defining, optimizing, and efficiently evaluating mathematical expressions involving multi-dimensional arrays.
jdb78/pytorch-forecasting
Time series forecasting with PyTorch
awslabs/gluonts
Probabilistic time series modeling in Python
tensorly/tensorly
TensorLy: Tensor Learning in Python.
PacktPublishing/Causal-Inference-and-Discovery-in-Python
Causal Inference and Discovery in Python by Packt Publishing
bharathgs/Awesome-pytorch-list
A comprehensive list of pytorch related content on github,such as different models,implementations,helper libraries,tutorials etc.
aangelopoulos/conformal-prediction
Lightweight, useful implementation of conformal prediction on real data.
BradyFU/Awesome-Multimodal-Large-Language-Models
:sparkles::sparkles:Latest Advances on Multimodal Large Language Models
BlinkDL/RWKV-LM
RWKV is an RNN with transformer-level LLM performance. It can be directly trained like a GPT (parallelizable). So it's combining the best of RNN and transformer - great performance, fast inference, saves VRAM, fast training, "infinite" ctx_len, and free sentence embedding.
acmi-lab/counterfactually-augmented-data
Learning the Difference that Makes a Difference with Counterfactually-Augmented Data
reactive-python/reactpy
It's React, but in Python