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Found 8 papers, 0 papers with code
Multi-user Co-inference with Batch Processing Capable Edge Server
To deal with the coupled offloading and scheduling introduced by concurrent batch processing, we first consider an offline problem with a constant edge inference latency and the same latency constraint.
Hierarchical Graph Convolutional Skeleton Transformer for Action Recognition
Graph convolutional networks (GCNs) have emerged as dominant methods for skeleton-based action recognition.
Joint Device Scheduling and Resource Allocation for Latency Constrained Wireless Federated Learning
Then, a greedy device scheduling algorithm is introduced, which in each step selects the device consuming the least updating time obtained by the optimal bandwidth allocation, until the lower bound begins to increase, meaning that scheduling more devices will degrade the model accuracy.
Adaptive Data Augmentation with Deep Parallel Generative Models
Data augmentation(DA) is a useful technique to enlarge the size of the training set and prevent overfitting for different machine learning tasks when training data is scarce.
Achieving Fairness in Determining Medicaid Eligibility through Fairgroup Construction
Effective complements to human judgment, artificial intelligence techniques have started to aid human decisions in complicated social problems across the world.
Deli-Fisher GAN: Stable and Efficient Image Generation With Structured Latent Generative Space
In this paper we propose the Deli-Fisher GAN, a GAN that generates photo-realistic images by enforcing structure on the latent generative space using similar approaches in \cite{deligan}.
Parallel Recurrent Data Augmentation for GAN training with Limited and Diverse Data
The need for large amounts of training image data with clearly defined features is a major obstacle to applying generative adversarial networks(GAN) on image generation where training data is limited but diverse, since insufficient latent feature representation in the already scarce data often leads to instability and mode collapse during GAN training.
Deep Generative Inpainting with Comparative Sample Augmentation
Recent advancements in deep learning techniques such as Convolutional Neural Networks(CNN) and Generative Adversarial Networks(GAN) have achieved breakthroughs in the problem of semantic image inpainting, the task of reconstructing missing pixels in given images.
