[NeurIPS 2024 Oral][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!

PDEBench: An Extensive Benchmark for Scientific Machine Learning

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

XQ-GAN🚀: An Open-source Image Tokenization Framework for Autoregressive Generation

Interactive Character Control with Auto-Regressive Motion Diffusion Models

Generative model for sequential recommendation based on Convolution Neural Networks (CNN))

SSR-Speech: Towards Stable, Safe and Robust Zero-shot Speech Editing and Synthesis

Fast and Robust Early-Exiting Framework for Autoregressive Language Models with Synchronized Parallel Decoding (EMNLP 2023 Long)

Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series to benchmark datasets from different domains

Official code for "Accelerating Feedforward Computation via Parallel Nonlinear Equation Solving", ICML 2021

A Unified Deep Model of Learning from both Data and Queries for Cardinality Estimation

PyTorch implementation for "Long Horizon Temperature Scaling", ICML 2023

Symbolic music generation taking inspiration from NLP and human composition process

Ultra-minimal autoregressive diffusion model for image generation

Recently inflation is a popular topic in Poland and is highest since 2001. Experts presume inflation in Poland should continue to rise, and by the end of 2021 it will be close to 8%. This notebook aims to develop a forecasting model for time series using Python.

PyTorch implementation for "Training and Inference on Any-Order Autoregressive Models the Right Way", NeurIPS 2022 Oral, TPM 2023 Best Paper Honorable Mention

Octree Transformer: Autoregressive 3D Shape Generation on Hierarchically Structured Sequences - CVPRW: StruCo3D, 2023

LayerDAG: A Layerwise Autoregressive Diffusion Model of Directed Acyclic Graphs

A enhanced Open Dialogue Context Generator supported by General Language Model Pretraining with Autoregressive Blank Infilling

Stochastic processes insights from VAE. Code for the paper: Learning minimal representations of stochastic processes with variational autoencoders.