Contrastive learning is a popular self-supervised learning technique that has shown remarkable success in training deep neural networks. The core idea behind contrastive learning is to learn representations that are not only discriminative but also invariant to various transformations. This is achieved by contrasting positive and negative samples in the embedding space.

My personal notes for studying diffusion models. Watching Dome's youtube video to learn.

Literature review on Unsupervised Representation Learning in diffusion models.

An interesting paper from ECCV2024. It talks about the color and shape disentanglement on Text-to-Image models. The solution is simple yet effective.

Revisit Contrastive learning Contrastive learning is an instance-wise discriminative approach that aims at making similar instances closer and dissim ...

T-cell receptors (TCR) bind to certain antigens found on abnormal cells. We can perform TCR engineering with VAEs.

ESM-1 and ESM-2 refer to protein language models developed by Facebook AI Research under the project name Evolutionary Scale Modeling (ESM). These models are designed to understand and predict various properties of proteins by leveraging the principles of language modeling in a manner similar to how models like GPT-3 process natural language.

Let's try to figure out whats inside AlphaFold! AlphaFold can accurately predict structures of biomolecular interactions.

ProteinDT is A Opensource Text-guided Protein Design Framework that uses Contrastive learning to align the two modalities.

The Axial Transformer is aimed at managing the complexity of high-dimensional data like images and videos, while The MSA transformer is focused on biological sequences and their alignments.