Categories: FAANG

STARFlow: Scaling Latent Normalizing Flows for High-resolution Image Synthesis

We present STARFlow, a scalable generative model based on normalizing flows that achieves strong performance in high-resolution image synthesis. The core of STARFlow is Transformer Autoregressive Flow (TARFlow), which combines the expressive power of normalizing flows with the structured modeling capabilities of Autoregressive Transformers. We first establish the theoretical universality of TARFlow for modeling continuous distributions. Building on this foundation, we introduce several key architectural and algorithmic innovations to significantly enhance scalability: (1) a deep-shallow…

Scalable Pre-training of Large Autoregressive Image Models

This paper introduces AIM, a collection of vision models pre-trained with an autoregressive objective. These models are inspired by their textual counterparts, i.e., Large Language Models (LLMs), and exhibit similar scaling properties. Specifically, we highlight two key findings: (1) the performance of the visual features scale with both the model…

February 2, 2024

In "FAANG"

Multimodal Autoregressive Pre-Training of Large Vision Encoders

*Equal Contributors A dominant paradigm in large multimodal models is to pair a large language de- coder with a vision encoder. While it is well-known how to pre-train and tune language decoders for multimodal tasks, it is less clear how the vision encoder should be pre-trained. A de facto standard…

November 23, 2024

In "FAANG"

Non-Autoregressive Neural Machine Translation: A Call for Clarity

Non-autoregressive approaches aim to improve the inference speed of translation models by only requiring a single forward pass to generate the output sequence instead of iteratively producing each predicted token. Consequently, their translation quality still tends to be inferior to their autoregressive counterparts due to several issues involving output token…

October 28, 2022

In "FAANG"

AI Generated Robotic Content