Summary of Hyperbolic Learning with Multimodal Large Language Models, by Paolo Mandica et al.
Hyperbolic Learning with Multimodal Large Language Models
by Paolo Mandica, Luca Franco, Konstantinos Kallidromitis, Suzanne Petryk, Fabio Galasso
First submitted to arxiv on: 9 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A medium-difficulty summary: Hyperbolic embeddings have been effective in capturing uncertainty and hierarchical relationships across various deep-learning tasks. This paper focuses on scaling multi-modal hyperbolic models, which are particularly challenging due to their large size and training complexity. The proposed BLIP-2 architecture is a hyperbolic version of the CLIP ViT-large model, consisting of hundreds of millions of parameters. The authors analyze the challenges of scaling these models and propose a novel training strategy that achieves comparable performance to its Euclidean counterpart while maintaining stability during training. This work highlights the potential insights into uncertainty offered by hyperbolic embeddings in modern vision-language models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A low-difficulty summary: This paper is about making computers better at understanding images and text. It’s trying to make a special kind of model that can learn from big amounts of data and understand how things are related. The challenge is that these models need to be very large and complicated, which makes them hard to train. The authors propose a new way to train this type of model that works well even with huge amounts of data. This could lead to computers being able to understand images and text in a more meaningful way. |
Keywords
» Artificial intelligence » Deep learning » Multi modal » Vit
