YorkNishi999
Ph.D. student at Wisconsin School of Business in Marketing Dept. Master of Science in Computer Science at UW-Madison, and BA in Econ/Stat at University of Tokyo
Madison, WI. USA.
YorkNishi999's Stars
meta-llama/llama3
The official Meta Llama 3 GitHub site
soulmachine/machine-learning-cheat-sheet
Classical equations and diagrams in machine learning
sanjib-sen/WebLaTex
A complete alternative for Overleaf with VSCode + Web + Git Integration + Copilot + Grammar & Spell Checker + Live Collaboration Support. Based on GitHub Codespace and Dev container.
susanathey/causalTree
Working repository for Causal Tree and extensions
jingwora/ChromaDB-Tutorial
uber-research/TuRBO
s-horiguchi/MahalanobisBatchBO
Kingsford-Group/vsbo
rees-c/PyREMBO
Python implementation of REMBO built on GPyTorch.
HideakiImamura/bo-book
ziyuw/rembo
Bayesian optimization in high-dimensions via random embedding.
openai/openai-cookbook
Examples and guides for using the OpenAI API
Yagami360/glossary-llm-chat-bot
RAG を使用した用語集応答 Slack ボット
tristandeleu/jax-dag-gflownet
Code for "Bayesian Structure Learning with Generative Flow Networks"
cumulo-autumn/StreamDiffusion
StreamDiffusion: A Pipeline-Level Solution for Real-Time Interactive Generation
zalandoresearch/ACIC23-competition
Data for and description of the ACIC 2023 data competition
makaishi2/python_bayes_intro
Pythonでスラスラわかるベイズ推論「超」入門 サポートサイト
vonhafften/problem_sets
Problem sets for economics and finance PhD coursework at UW-Madison.
ericschulman/metrics
Python Code for UTexas econometrics
jsyoon0823/GANITE
Codebase for GANITE: Estimation of Individualized Treatment Effects using GANs - ICLR 2018
k-metrics/cabinet
Project Cabinet (Japanese Only)
lllyasviel/ControlNet
Let us control diffusion models!
woshidandan/TANet-image-aesthetics-and-quality-assessment
[IJCAI 2022, Official Code] for paper "Rethinking Image Aesthetics Assessment: Models, Datasets and Benchmarks". Official Weights and Demos provided. 首个面向多主题场景的美学评估数据集、算法和benchmark.
rlabbe/Kalman-and-Bayesian-Filters-in-Python
Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filters, and more. All exercises include solutions.
ghmagazine/llm-book
「大規模言語モデル入門」(2023)と「大規模言語モデル入門Ⅱ〜生成型LLMの実装と評価」(2024)のGitHubリポジトリ
huggingface/peft
🤗 PEFT: State-of-the-art Parameter-Efficient Fine-Tuning.
zjunlp/DART
[ICLR 2022] Differentiable Prompt Makes Pre-trained Language Models Better Few-shot Learners
keirp/automatic_prompt_engineer
google-research/prompt-tuning
Original Implementation of Prompt Tuning from Lester, et al, 2021
matplotlib/cheatsheets
Official Matplotlib cheat sheets