Search Results for author:
Found 7 papers, 4 papers with code
StableMask: Refining Causal Masking in Decoder-only Transformer
The decoder-only Transformer architecture with causal masking and relative position encoding (RPE) has become the de facto choice in language modeling.
Scientific Large Language Models: A Survey on Biological & Chemical Domains
Large Language Models (LLMs) have emerged as a transformative power in enhancing natural language comprehension, representing a significant stride toward artificial general intelligence.
InstructProtein: Aligning Human and Protein Language via Knowledge Instruction
To address this challenge, we propose InstructProtein, an innovative LLM that possesses bidirectional generation capabilities in both human and protein languages: (i) taking a protein sequence as input to predict its textual function description and (ii) using natural language to prompt protein sequence generation.
Graph Sampling-based Meta-Learning for Molecular Property Prediction
To effectively utilize many-to-many correlations of molecules and properties, we propose a Graph Sampling-based Meta-learning (GS-Meta) framework for few-shot molecular property prediction.
Molecular Contrastive Learning with Chemical Element Knowledge Graph
To address these issues, we construct a Chemical Element Knowledge Graph (KG) to summarize microscopic associations between elements and propose a novel Knowledge-enhanced Contrastive Learning (KCL) framework for molecular representation learning.
Knowledge-aware Contrastive Molecular Graph Learning
Leveraging domain knowledge including fingerprints and functional groups in molecular representation learning is crucial for chemical property prediction and drug discovery.
