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18 papers · 1 filter
Estimating Mutual Information between Time Series and Temporal Event Sequences Across Diverse Analysis Tasks
Haoji Hu, Huaqing Mao, Yijun Lin +4
Pairwise dependence measures such as correlation and causality are fundamental to temporal data mining, yet there is still no principled and robust way to quantify dependence betwe…
Relaxed Efficient Acquisition of Context and Temporal Features
Yunni Qu, Dzung Dinh, Grant King +5
In many biomedical applications, measurements are not freely available at inference time: each laboratory test, imaging modality, or assessment incurs financial cost, time burden,…
LayerPipe2: Multistage Pipelining and Weight Recompute via Improved Exponential Moving Average for Training Neural Networks
Nanda K. Unnikrishnan, Keshab K. Parhi
In our prior work, LayerPipe, we had introduced an approach to accelerate training of convolutional, fully connected, and spiking neural networks by overlapping forward and backwar…
DR-CircuitGNN: Training Acceleration of Heterogeneous Circuit Graph Neural Network on GPUs
Yuebo Luo, Shiyang Li, Junran Tao +6
The increasing scale and complexity of integrated circuit design have led to increased challenges in Electronic Design Automation (EDA). Graph Neural Networks (GNNs) have emerged a…
Towards Collaborative Fairness in Federated Learning Under Imbalanced Covariate Shift
Tianrun Yu, Jiaqi Wang, Haoyu Wang +4
Collaborative fairness is a crucial challenge in federated learning. However, existing approaches often overlook a practical yet complex form of heterogeneity: imbalanced covariate…
Novel Batch Active Learning Approach and Its Application to Synthetic Aperture Radar Datasets
James Chapman, Bohan Chen, Zheng Tan +3
Active learning improves the performance of machine learning methods by judiciously selecting a limited number of unlabeled data points to query for labels, with the aim of maximal…