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Found 8 papers, 3 papers with code
Uni-SMART: Universal Science Multimodal Analysis and Research Transformer
Scientific literature often includes a wide range of multimodal elements, such as molecular structure, tables, and charts, which are hard for text-focused LLMs to understand and analyze.
SciAssess: Benchmarking LLM Proficiency in Scientific Literature Analysis
Recent breakthroughs in Large Language Models (LLMs) have revolutionized natural language understanding and generation, igniting a surge of interest in leveraging these technologies in the field of scientific literature analysis.
DCQA: Document-Level Chart Question Answering towards Complex Reasoning and Common-Sense Understanding
Our DCQA dataset is expected to foster research on understanding visualizations in documents, especially for scenarios that require complex reasoning for charts in the visually-rich document.
Progressive Evidence Refinement for Open-domain Multimodal Retrieval Question Answering
Firstly, utilizing compressed evidence features as input to the model results in the loss of fine-grained information within the evidence.
Deep Molecular Representation Learning via Fusing Physical and Chemical Information
To push the boundaries of molecular representation learning, we present PhysChem, a novel neural architecture that learns molecular representations via fusing physical and chemical information of molecules.
Equivalent Distance Geometry Error for Molecular Conformation Comparison
Straight-forward conformation generation models, which generate 3-D structures directly from input molecular graphs, play an important role in various molecular tasks with machine learning, such as 3D-QSAR and virtual screening in drug design.
HamNet: Conformation-Guided Molecular Representation with Hamiltonian Neural Networks
Well-designed molecular representations (fingerprints) are vital to combine medical chemistry and deep learning.
Conformation-Guided Molecular Representation with Hamiltonian Neural Networks
Well-designed molecular representations (fingerprints) are vital to combine medical chemistry and deep learning.
