[NeurIPS 2024 Best Paper][GPT beats diffusion🔥] [scaling laws in visual generation📈] Official impl. of "Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Prediction". An *ultra-simple, user-friendly yet state-of-the-art* codebase for autoregressive image generation!

From anything to mesh like human artists. Official impl. of "MeshAnything: Artist-Created Mesh Generation with Autoregressive Transformers"

Autoregressive Model Beats Diffusion: 🦙 Llama for Scalable Image Generation

Infinity ∞ : Scaling Bitwise AutoRegressive Modeling for High-Resolution Image Synthesis

Generative Models Tutorial with Demo: Bayesian Classifier Sampling, Variational Auto Encoder (VAE), Generative Adversial Networks (GANs), Popular GANs Architectures, Auto-Regressive Models, Important Generative Model Papers, Courses, etc..

[NeurIPS 2024]OmniTokenizer: one model and one weight for image-video joint tokenization.

Official implementation of X-Prompt: Towards Universal In-Context Image Generation in Auto-Regressive Vision Language Foundation Models

Official PyTorch Implementation of "Scalable Autoregressive Image Generation with Mamba"

Phase-amplitude coupling (PAC) toolbox

CoDe: Collaborative Decoding Makes Visual Auto-Regressive Modeling Efficient

Heart rate variability (HRV) analysis tool to detect autonomic nerve state

Covid-19 India's statewide analysis with census data 2011 and Kaggle data

Auto-regressive causal language model for molecule (SMILES) and reaction template (SMARTS) generation based on the Hugging Face implementation of OpenAI's GPT-2 transformer decoder model

Implementing Time Series Auto Regressive Model (AR Model) from Scratch in Python

About Code for the paper "NASH: A Simple Unified Framework of Structured Pruning for Accelerating Encoder-Decoder Language Models" (EMNLP 2023 Findings)

Graph neural network models to perform link prediction on the nba and ncaa point differential graph on seasons 2013-2019, 2021.

AI Comic Factory - Generate Comics with AI, 🦙 Llama for Scalable Anime Generation, Image Generation, Comic Generation and Game Generation

Forecast airline passenger demand using time series models like AR, ARMA, and LSTM to improve operations, optimize scheduling, enhance resource allocation, and streamline supply chain management through accurate demand predictions

📈 Application of neural networks in the prediction of PETR4.SA shares, compared with statistical models

This repository focuses on predicting Apple Inc.'s stock prices using time series analysis from 2013 to 2018. The project involves data exploration, stationarity analysis, time series decomposition, model development, and generating insights to assist investors.