Jae Hee Lee (dschaehi)

dschaehi

Geek Repo

Company:University of Hamburg

Location:Hamburg, Germany

Home Page:https://jaeheelee.gitlab.io/

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Jae Hee Lee's repositories

capturing-word-order

Capturing Word Order in Averaging Based Sentence Embeddings

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CLEVRER

PyTorch implementation of ICLR 2020 paper "CLEVRER: CoLlision Events for Video REpresentation and Reasoning"

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film

FiLM: Visual Reasoning with a General Conditioning Layer

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nn-zero-to-hero

Neural Networks: Zero to Hero

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causal-ml

Must-read papers and resources related to causal inference and machine (deep) learning

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Causal_Reading_Group

We will keep updating the paper list about machine learning + causal theory. We also internally discuss related papers between NExT++ (NUS) and LDS (USTC) by week.

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dfg

A LaTeX template for a basic DFG (Deutsche Forschungsgemeinschaft, German Research Foundation) grant proposal.

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dlwpt-code

Code for the book Deep Learning with PyTorch by Eli Stevens, Luca Antiga, and Thomas Viehmann.

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easy-VQA

The Easy Visual Question Answering dataset.

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hugo-researcher

A simple resume theme for Hugo

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lit-gpt

Hackable implementation of state-of-the-art open-source LLMs based on nanoGPT. Supports flash attention, 4-bit and 8-bit quantization, LoRA and LLaMA-Adapter fine-tuning, pre-training. Apache 2.0-licensed.

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mac-network-pytorch

Memory, Attention and Composition (MAC) Network for CLEVR implemented in PyTorch

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micrograd

A tiny scalar-valued autograd engine and a neural net library on top of it with PyTorch-like API

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NetDissect-Lite

Light version of Network Dissection for Quantifying Interpretability of Networks

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nlxgpt

NLX-GPT: A Model for Natural Language Explanations in Vision and Vision-Language Tasks, CVPR 2022 (Oral)

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Quantus

Quantus is an eXplainable AI toolkit for responsible evaluation of neural network explanations

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soho

Minimalist Hugo theme based on Hyde

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Transformers-Tutorials

This repository contains demos I made with the Transformers library by HuggingFace.

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vit-pytorch

Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch

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