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TLDR AI

TLDR AI 2023-02-24

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01HEADLINES & LAUNCHES

TOME RAISES $43M SERIES B FOR AI POWERED STORY TELLING

A mix between powerpoint, figma, and docs - Tome is a creative tool powered by generative technologies. It has been growing quickly and looks like a potentially interesting combination of several ideas that have been in the community for the past several months. This brings their total money raised to just over $75m.

bit.ly3 min read

02RESEARCH & INNOVATION

LATENT VIDEO DIFFUSION MODELS

One of the main bottlenecks of generative video work is the inability of current methods to scale to the massive number of frames or maintain temporal consistency in generation. This work tries to solve both problems at the same time by working in a projective latent space. They obtain impressive FID scores on standard benchmarks and have fun videos on their project page.

arxiv.org21 min read

DRAMATICALLY IMPROVES QUALITY

By building on the fascinating “score based” interpretation of diffusion models, this work investigates why diffusion models fail at various tasks that require composition. They determine that the sampler, and not the underlying model, must be the source of error. They then propose a new energy-based parameterization and Metropolis-corrected sampler. They show strong improvement on a variety of tough tasks. Also, as a smaller win, they have a nice background section on all these topics!

arxiv.org28 min read

TASKS

Modular deep learning has emerged as a promising solution for developing models that specialize in multiple tasks without incurring negative interference and that generalize systematically to non-identically distributed tasks. This article provides a survey of modular architectures, explores various additional purposes of modularity, and reports various concrete applications where modularity has been successfully deployed.

arxiv.org30 min read

TUNING

The article discusses the use of parameter-efficient adaptation methods such as prompt tuning (PT), in-context learning (ICL), and instruction prompt tuning (IPT) for fine-tuning large language models. The paper empirically studies when and how in-context examples improve prompt tuning, and observes that IPT does not always outperform PT, PT is unstable, and prompts learned for a specific source task via PT exhibit positive transfer when paired with in-context examples of a different target task.

arxiv.org12 min read

03ENGINEERING & RESOURCES

PAPERS ON THE UX OF AI PROGRAMMING ASSISTANTS

It could be argued that in addition to the powerful model, co-pilot’s success is because of the innovative user experience. In any case, this page provides a list of papers that study the effectiveness of different choices on users of programming assistant tools.

austinhenley.com2 min read

The article presents a method called Vid2Avatar, which learns human avatars from monocular in-the-wild videos without the need for ground truth supervision, external segmentation modules, or priors extracted from large datasets. The method solves the task of surface reconstruction and scene decomposition by jointly modeling the human and background in the scene using two separate neural fields.

moygcc.github.io4 min read

MOCHIDIFFUSION

This app brings stable diffusion to Macs. This app uses Apple's Core ML Stable Diffusion implementation to achieve maximum performance and speed on Apple Silicon based Macs while reducing memory requirements. It also runs on Intel based Macs too.

github.comGitHub repository

04MISCELLANEOUS