Search Results for author:
Found 9 papers, 4 papers with code
Larimar: Large Language Models with Episodic Memory Control
Efficient and accurate updating of knowledge stored in Large Language Models (LLMs) is one of the most pressing research challenges today.
ProtIR: Iterative Refinement between Retrievers and Predictors for Protein Function Annotation
Protein function annotation is an important yet challenging task in biology.
Structure-Informed Protein Language Model
To address this issue, we introduce the integration of remote homology detection to distill structural information into protein language models without requiring explicit protein structures as input.
Learning Granger Causality from Instance-wise Self-attentive Hawkes Processes
In particular, we are interested in discovering instance-level causal structures in an unsupervised manner.
A Systematic Study of Joint Representation Learning on Protein Sequences and Structures
Recent sequence representation learning methods based on Protein Language Models (PLMs) excel in sequence-based tasks, but their direct adaptation to tasks involving protein structures remains a challenge.
Pre-Training Protein Encoder via Siamese Sequence-Structure Diffusion Trajectory Prediction
Considering the essential protein conformational variations, we enhance DiffPreT by a method called Siamese Diffusion Trajectory Prediction (SiamDiff) to capture the correlation between different conformers of a protein.
Directed Graph Auto-Encoders
We introduce a new class of auto-encoders for directed graphs, motivated by a direct extension of the Weisfeiler-Leman algorithm to pairs of node labels.
Simultaneous Parameter Learning and Bi-Clustering for Multi-Response Models
We consider multi-response and multitask regression models, where the parameter matrix to be estimated is expected to have an unknown grouping structure.
Understanding Innovation to Drive Sustainable Development
Innovation is among the key factors driving a country's economic and social growth.
