song-seng-hun Goto Github PK

followers: 23.0 following: 235.0 repos: 24.0 gists: 0.0

Name: 송승훈

Type: User

Blog: https://www.notion.so/Seunghun-Song-926309624b8643849f59a13fed17138a

송승훈's Projects

awesome-prompting-on-vision-language-model

This repo lists relevant papers summarized in our survey paper: A Systematic Survey of Prompt Engineering on Vision-Language Foundation Models.

backprop-alts

This repository has implementations of various alternatives to backpropagation for training neural networks.

customizedspikegpt

original model : Implementation of "SpikeGPT: Generative Pre-trained Language Model with Spiking Neural Networks"

decision-transformer

Official codebase for Decision Transformer: Reinforcement Learning via Sequence Modeling.

gpt-ros

making decision (schedule of robot) using ChatGPT API

jepa

Experiments in Joint Embedding Predictive Architectures (JEPAs).

memformer

Implementation of Memformer, a Memory-augmented Transformer, in Pytorch

memtorch

A Simulation Framework for Memristive Deep Learning Systems

minimalrl

Implementations of basic RL algorithms with minimal lines of codes! (pytorch based)

The repository collects many various multi-modal transformer architectures, including image transformer, video transformer, image-language transformer, video-language transformer and self-supervised learning models. Additionally, it also collects many useful tutorials and tools in these related domains.

my-eda-project

EDA 프로젝트 4조 저장소. 🎲 보드게임 트렌드 분석 🎲

my_iot_project

V2I

pynput

Sends virtual input commands

pywinauto

Windows GUI Automation with Python (based on text properties)

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.

spiking-neural-network

Pure python implementation of SNN

spiking-neural-network-snn-with-pytorch-where-backpropagation-engenders-stdp

What about coding a Spiking Neural Network using an automatic differentiation framework? In SNNs, there is a time axis and the neural network sees data throughout time, and activation functions are instead spikes that are raised past a certain pre-activation threshold. Pre-activation values constantly fades if neurons aren't excited enough.

thought-and-memory

Investigations into transformers with memory.

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