33 citations · 212 across the 29 of their papers we have counts for
7 papers · 1 filter
Hierarchical Grammar-Induced Geometry for Data-Efficient Molecular Property Prediction
Minghao Guo, Veronika Thost, Samuel W Song +4
The prediction of molecular properties is a crucial task in the field of material and drug discovery. The potential benefits of using deep learning techniques are reflected in the…
The Impact of Positional Encoding on Length Generalization in Transformers
Amirhossein Kazemnejad, Inkit Padhi, Karthikeyan Natesan Ramamurthy +2
Length generalization, the ability to generalize from small training context sizes to larger ones, is a critical challenge in the development of Transformer-based language models.…
Efficient Equivariant Transfer Learning from Pretrained Models
Sourya Basu, Pulkit Katdare, Prasanna Sattigeri +4
Efficient transfer learning algorithms are key to the success of foundation models on diverse downstream tasks even with limited data. Recent works of Basu et al. (2023) and Kaba e…
A Systematic Study of Joint Representation Learning on Protein Sequences and Structures
Zuobai Zhang, Chuanrui Wang, Minghao Xu +4
Learning effective protein representations is critical in a variety of tasks in biology such as predicting protein functions. Recent sequence representation learning methods based…
AI Maintenance: A Robustness Perspective
Pin-Yu Chen, Payel Das
With the advancements in machine learning (ML) methods and compute resources, artificial intelligence (AI) empowered systems are becoming a prevailing technology. However, current…
Reprogramming Pretrained Language Models for Protein Sequence Representation Learning
Ria Vinod, Pin-Yu Chen, Payel Das
Machine Learning-guided solutions for protein learning tasks have made significant headway in recent years. However, success in scientific discovery tasks is limited by the accessi…