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Found 12 papers, 2 papers with code
Improving Implicit Regularization of SGD with Preconditioning for Least Square Problems
In this paper, we study the generalization performance of SGD with preconditioning for the least squared problem.
BG-HGNN: Toward Scalable and Efficient Heterogeneous Graph Neural Network
Many computer vision and machine learning problems are modelled as learning tasks on heterogeneous graphs, featuring a wide array of relations from diverse types of nodes and edges.
On the Topology Awareness and Generalization Performance of Graph Neural Networks
Using this framework, we investigate the effects of topology awareness on GNN generalization performance.
MSPipe: Efficient Temporal GNN Training via Staleness-aware Pipeline
However, the iterative reading and updating process of the memory module in MTGNNs to obtain up-to-date information needs to follow the temporal dependencies.
Towards Robust Graph Incremental Learning on Evolving Graphs
We provide a formal formulation and analysis of the problem, and propose a novel regularization-based technique called Structural-Shift-Risk-Mitigation (SSRM) to mitigate the impact of the structural shift on catastrophic forgetting of the inductive NGIL problem.
PRES: Toward Scalable Memory-Based Dynamic Graph Neural Networks
Memory-based Dynamic Graph Neural Networks (MDGNNs) are a family of dynamic graph neural networks that leverage a memory module to extract, distill, and memorize long-term temporal dependencies, leading to superior performance compared to memory-less counterparts.
MTRGL:Effective Temporal Correlation Discerning through Multi-modal Temporal Relational Graph Learning
In this study, we explore the synergy of deep learning and financial market applications, focusing on pair trading.
CDMPP: A Device-Model Agnostic Framework for Latency Prediction of Tensor Programs
Considering the large space of DNN models and devices that impede direct profiling of all combinations, recent efforts focus on building a predictor to model the performance of DNN models on different devices.
Towards Robust Inductive Graph Incremental Learning via Experience Replay
Inductive node-wise graph incremental learning is a challenging task due to the dynamic nature of evolving graphs and the dependencies between nodes.
ABC: Aggregation before Communication, a Communication Reduction Framework for Distributed Graph Neural Network Training and Effective Partition
ABC method exploits the permutation-invariant property of the GNNs layer and leads to a paradigm where vertex-cut is proved to admit a superior communication performance than the currently popular paradigm (edge-cut).
On Locality in Graph Learning via Graph Neural Network
In this paper, we study how the training set in the input graph effects the performance of GNN.
Deep Learning System to Screen Coronavirus Disease 2019 Pneumonia
We found that the real time reverse transcription-polymerase chain reaction (RT-PCR) detection of viral RNA from sputum or nasopharyngeal swab has a relatively low positive rate in the early stage to determine COVID-19 (named by the World Health Organization).
