Dissecting the ESM3 Model Architecture
Alternatively titled: dissecting how multimodality neural networks work!
Hello fellow datanistas!
Recently, I’ve been exploring multi-modality neural network models. One question has repeatedly come up: how do we handle missing data during training? I recently did a deep dive into the ESM3 model, a fascinating example of such a model, and I've shared my insights in a detailed blog post. In it, I explore the intricacies of the model's architecture, input handling, and the ways it deals with absent modalities.
Whether you're a seasoned expert or a curious enthusiast in the field of machine learning and bioinformatics, I think there may be something of interest in there.
Please check out the post! I’m curious to hear your thoughts on it too. Here's the link to the blog post: Dissecting the ESM3 Model Architecture.
If you find the post insightful, please feel free to forward it to others in your network who might appreciate the deep dive into such a complex topic.
Looking forward to hearing your thoughts and continuing our exploration of cutting-edge data science topics together!
Cheers,
Eric