ArXiv Dives: I-JEPA
Today, we’re diving into the I-JEPA paper. JEPA stands for Joint-Embedding Predictive Architecture and if you have been following
How to train Mistral 7B as a "Self-Rewarding Language Model"
About a month ago we went over the "Self-Rewarding Language Models" paper by the team at Meta AI
Downloading Datasets with Oxen.ai
Oxen.ai makes it quick and easy to download any version of your data wherever and whenever you need it.
Uploading Datasets to Oxen.ai
Oxen.ai makes it quick and easy to upload your datasets, keep track of every version and share them with
ArXiv Dives - Diffusion Transformers
Diffusion transformers achieve state-of-the-art quality generating images by replacing the commonly used U-Net backbone with a transformer that operates on
"Road to Sora" Paper Reading List
This post is an effort to put together a reading list for our Friday paper club called ArXiv Dives. Since
ArXiv Dives - Medusa
Abstract
In this paper, they present MEDUSA, an efficient method that augments LLM inference by adding extra decoding heads to
ArXiv Dives - Lumiere
This paper introduces Lumiere – a text-to-video diffusion model designed for synthesizing videos that portray realistic, diverse and coherent motion – a
ArXiv Dives - Depth Anything
This paper presents Depth Anything, a highly practical solution for robust monocular depth estimation. Depth estimation traditionally requires extra hardware
Arxiv Dives - Toolformer: Language models can teach themselves to use tools
Large Language Models (LLMs) show remarkable capabilities to solve new tasks from a few textual instructions, but they also paradoxically
