심정우, Jungwoo Shim (jshim0978)

jshim0978

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Company:Student

Location:Seoul, Korea

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심정우, Jungwoo Shim's repositories

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RUSURE

hallucination mitigation

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Applications_NN

Repository of lecture "Applications and Practice in Neural Networks (XAI606)"

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Awesome-CV-jws

:page_facing_up: Awesome CV is LaTeX template for your outstanding job application

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awesome-machine-learning-interpretability

A curated list of awesome machine learning interpretability resources.

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BackdoorBox

The open-sourced Python toolbox for backdoor attacks and defenses.

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color_poisoning

course project for Applications and Practice of Neural Networks

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ControlNet

Let us control diffusion models!

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Diff-Pruning

Structural Pruning for Diffusion Models

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embedchain

Framework to easily create LLM powered bots over any dataset.

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free-programming-books

:books: Freely available programming books

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hallucination-leaderboard

Leaderboard Comparing LLM Performance at Producing Hallucinations when Summarizing Short Documents

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KU_NN

Neural Networks assignments

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llama

Inference code for LLaMA models

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LLaMA-Factory

Easy-to-use LLM fine-tuning framework (LLaMA, BLOOM, Mistral, Baichuan, Qwen, ChatGLM)

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LLM-Pruner

LLM-Pruner: On the Structural Pruning of Large Language Models

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ml_models

A collection of pre-trained, state-of-the-art models in the ONNX format

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OpenLLM

Operating LLMs in production

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pytorch-grad-cam

Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.

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pytorch-tutorial

PyTorch Tutorial for Deep Learning Researchers

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sort-google-scholar

Sorting Google Scholar search results based on the number of citations

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transformers

🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.

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uvadlc_notebooks

Repository of Jupyter notebook tutorials for teaching the Deep Learning Course at the University of Amsterdam (MSc AI), Fall 2022/Spring 2022

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Woodpecker

✨✨Woodpecker: Hallucination Correction for MLLMs. The first work to correct hallucination in multimodal large language models.

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