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Found 6 papers, 3 papers with code
Efficient Multilingual Multi-modal Pre-training through Triple Contrastive Loss
In this multilingual approach, a typical setup is to use pairs of (image and English-text) and translation pairs.
BOK-VQA: Bilingual outside Knowledge-Based Visual Question Answering via Graph Representation Pretraining
The current research direction in generative models, such as the recently developed GPT4, aims to find relevant knowledge information for multimodal and multilingual inputs to provide answers.
Solvent: A Framework for Protein Folding
We hope that Solvent will increase the reliability and consistency of proposed models and give efficiency in both speed and costs, resulting in acceleration on protein folding modeling research.
ProtFIM: Fill-in-Middle Protein Sequence Design via Protein Language Models
With the evaluation of existing models on the benchmark, we reveal the weakness of existing language models and show that language models trained via fill-in-middle transformation, called ProtFIM, are more appropriate for protein engineering.
Deep learning models for predicting RNA degradation via dual crowdsourcing
Furthermore, these models generalized to blindly predicting orthogonal degradation data on much longer mRNA molecules (504-1588 nucleotides) with improved accuracy compared to previously published models.
A community-powered search of machine learning strategy space to find NMR property prediction models
The rise of machine learning (ML) has created an explosion in the potential strategies for using data to make scientific predictions.
Ranked #1 on
NMR J-coupling
on QM9
