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Found 7 papers, 2 papers with code
A Survey of Geometric Graph Neural Networks: Data Structures, Models and Applications
Geometric graph is a special kind of graph with geometric features, which is vital to model many scientific problems.
Equivariant Pretrained Transformer for Unified Geometric Learning on Multi-Domain 3D Molecules
Pretraining on a large number of unlabeled 3D molecules has showcased superiority in various scientific applications.
Rigid Protein-Protein Docking via Equivariant Elliptic-Paraboloid Interface Prediction
The study of rigid protein-protein docking plays an essential role in a variety of tasks such as drug design and protein engineering.
Beyond Efficiency: A Systematic Survey of Resource-Efficient Large Language Models
We categorize methods based on their optimization focus: computational, memory, energy, financial, and network resources and their applicability across various stages of an LLM's lifecycle, including architecture design, pretraining, finetuning, and system design.
Staleness-Alleviated Distributed GNN Training via Online Dynamic-Embedding Prediction
It utilizes an offline memory to cache historical information (e. g., node embedding) as an affordable approximation of the exact value and achieves high concurrency.
DevelSet: Deep Neural Level Set for Instant Mask Optimization
To further enhance printability and fast iterative convergence, we propose a novel deep neural network delicately designed with level set intrinsic principles to facilitate the joint optimization of DNN and GPU accelerated level set optimizer.
AdaOPC: A Self-Adaptive Mask Optimization Framework For Real Design Patterns
We inspect the pattern distribution on a design layer and find that different sub-regions have different pattern complexity.
