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Found 15 papers, 3 papers with code
HiGraphDTI: Hierarchical Graph Representation Learning for Drug-Target Interaction Prediction
Specifically, HiGraphDTI learns hierarchical drug representations from triple-level molecular graphs to thoroughly exploit chemical information embedded in atoms, motifs, and molecules.
GNNavigator: Towards Adaptive Training of Graph Neural Networks via Automatic Guideline Exploration
Graph Neural Networks (GNNs) succeed significantly in many applications recently.
Graph Neural Networks Automated Design and Deployment on Device-Edge Co-Inference Systems
GCoDE abstracts the device communication process into an explicit operation and fuses the search of architecture and the operations mapping in a unified space for joint-optimization.
Multi-Label Adaptive Batch Selection by Highlighting Hard and Imbalanced Samples
However, the intrinsic class imbalance in multi-label data may bias the model towards majority labels, since samples relevant to minority labels may be underrepresented in each mini-batch.
FedRDMA: Communication-Efficient Cross-Silo Federated LLM via Chunked RDMA Transmission
Communication overhead is a significant bottleneck in federated learning (FL), which has been exaggerated with the increasing size of AI models.
Architectural Implications of GNN Aggregation Programming Abstractions
Graph neural networks (GNNs) have gained significant popularity due to the powerful capability to extract useful representations from graph data.
EdgeMoE: Fast On-Device Inference of MoE-based Large Language Models
Large Language Models (LLMs) such as GPTs and LLaMa have ushered in a revolution in machine intelligence, owing to their exceptional capabilities in a wide range of machine learning tasks.
Hardware-Aware Graph Neural Network Automated Design for Edge Computing Platforms
Moreover, HGNAS achieves hardware awareness during the GNN architecture design by leveraging a hardware performance predictor, which could balance the GNN model accuracy and efficiency corresponding to the characteristics of targeted devices.
Towards Sustainable Satellite Edge Computing
Recently, Low Earth Orbit (LEO) satellites experience rapid development and satellite edge computing emerges to address the limitation of bent-pipe architecture in existing satellite systems.
Benchmarking of DL Libraries and Models on Mobile Devices
Deploying deep learning (DL) on mobile devices has been a notable trend in recent years.
Optimizing Memory Efficiency of Graph Neural Networks on Edge Computing Platforms
Graph neural networks (GNN) have achieved state-of-the-art performance on various industrial tasks.
Hierarchical Federated Learning through LAN-WAN Orchestration
Our proposed FL can accelerate the learning process and reduce the monetary cost with frequent local aggregation in the same LAN and infrequent global aggregation on a cloud across WAN.
DP-Net: Dynamic Programming Guided Deep Neural Network Compression
In this work, we propose an effective scheme (called DP-Net) for compressing the deep neural networks (DNNs).
Fast and robust misalignment correction of Fourier ptychographic microscopy
Fourier ptychographi cmicroscopy(FPM) is a newly developed computational imaging technique that can provide gigapixel images with both high resolution (HR) and wide field of view (FOV).
