Quantized inference code for LLaMA models

The official repository for <Autoencoding Under Normalization Constraints> (Yoon, Noh and Park, ICML 2021).

Papers to look at when starting Vision Transformer 📚

Generic template to bootstrap your PyTorch project.

Deep Learning project template best practices with Pytorch Lightning, Hydra, Tensorboard.

PyTorch Lightning + Hydra. A very user-friendly template for ML experimentation. ⚡🔥⚡

Simple project base template for PyTorch deep Learning project. Features clean implementation of DDP training and Hydra config.

An Agile RISC-V SoC Design Framework with in-order cores, out-of-order cores, accelerators, and more

This is originally a collection of papers on neural network accelerators. Now it's more like my selection of research on deep learning and computer architecture.

키움 증권 조건 검색식 사용 Console App

Probabilistic time series modeling in Python

StarGAN v2 - Official PyTorch Implementation (CVPR 2020)

Python code parsing data from PhysioNet Challenge 2012

An open-source NLP research library, built on PyTorch.

pip install antialiased-cnns to improve stability and accuracy

PyTorch reimplementation of Interactive Deep Colorization

ICRA 2018 "Sparse-to-Dense: Depth Prediction from Sparse Depth Samples and a Single Image" (PyTorch Implementation)

Encoder-Decoder for Face Completion based on Gated Convolution

Residual attention generative adversarial networks for image-to-image translation

Neural network graphs and training metrics for PyTorch, Tensorflow, and Keras.

A library for ML benchmarking. It's powerful.

A set of examples around pytorch in Vision, Text, Reinforcement Learning, etc.

Source Code for 'Beginning Anomaly Detection Using Python-Based Deep Learning' by Sridhar Alla and Suman Kalyan Adari

Sequence modeling benchmarks and temporal convolutional networks

Code and models accompanying "Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning"

Pytorch implementations of various types of autoencoders

Code for the paper: CNN-generated images are surprisingly easy to spot... for now https://peterwang512.github.io/CNNDetection/

GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training

Benchmarks for Characterizing the Deployment of Deep Neural Networks on Commercial Edge Devices - IISWC'19 Paper