AlphaFold2 is the strongest tool today to predict protein 3D structure. It will be massively used to bring the world protein research forward. Since it is very complex and contains many new architectures, in this post, we gradually decompose the system into its base components.
(Deep) Neural Network architecture, training, tuning and fine-tuning, reasoning, validating and inferencing.
This paper suggests a new computationally efficient method for constructing low-dimensional representation of unlabeled data.
A review and clear explanation of the NeRF method, which can be used to synthesize 3D scenes out of an input image. This method is the base for many other research methods that followed.
Yam Peleg examines a kaggle solution using convolutional neural networks which can process tabular data while being columns order agnostic.
To overcome Transformers' squared complexity (w.r.t input length), the Perceiver article here offers a novel method to learn the QKV matrices. Check it out!