Heartbreak for Anish Giri!! Played so well the whole tournament only to lose a completely winning game in armageddon on time

I'll be presenting this work at #KDD2021 in the research paper session on 08/17 at 1:30 pm EST and in the poster session on 08/18 at 5:30 pm EST. Please feel free to drop by if you're attending, or ping me if you have any questions!

Excited to share that our workshop proposal on "Learning from Time Series for Health" got accepted to NeurIPS Conference 2022.

10 days to submit to our #NeurIPS workshop "Learning from Time Series for Health". Checkout our website: timeseriesforhealth.github.io Can't wait to see your amazing work and meet you in person in December!

By popular demand, we've extended the submission deadline for the #NeurIPS2022 Workshop on Learning from Time Series for Health to September 30th!

We’re pleased to introduce Make-A-Video, our latest in #GenerativeAI research! With just a few words, this state-of-the-art AI system generates high-quality videos from text prompts. Have an idea you want to see? Reply w/ your prompt using #MetaAI and we’ll share more results.

Come join us today for the ‘Learning from time series for health’ workshop at #NeurIPS2022 Room 392

Our paper on “Learning to Localize Objects Improves Spatial Reasoning in Visual-LLMs” accepted to CVPR ‘24. Arxiv: arxiv.org/abs/2404.07449

Check out our recent CVPR paper on improving spatial reasoning in Visual-LLMs.

We presented Belebele at ACL 2024 this week! (Thx to Davis Liang and Satya Narayan Shukla) A year on from its release, it’s been really cool to see the diversity of research projects that have used it. The field is in dire need of more multilingual benchmarks !

Our take on a 4o-style AR + diffusion unified model: Transferring knowledge from an AR LLM to generation is easier than expected--you don't even need to touch the LLM. The right bridge between output modalities can unlock cool capabilities like knowledge-augmented generation!

Check out our latest work on training unified understanding and generation models. We show that frozen MLLMs can seamlessly transfer knowledge, reasoning, and in-context learning from text to pixel output.

Due to popular demand, we have open-sourced the code and data for MetaQuery.