64 citations · 252 across the 64 of their papers we have counts for
9 papers · 1 filter
LegoDNN: Block-grained Scaling of Deep Neural Networks for Mobile Vision
Rui Han, Qinglong Zhang, Chi Harold Liu +3
Deep neural networks (DNNs) have become ubiquitous techniques in mobile and embedded systems for applications such as image/object recognition and classification. The trend of exec…
Locality Sensitive Hash Aggregated Nonlinear Neighbourhood Matrix Factorization for Online Sparse Big Data Analysis
Zixuan Li, Hao Li, Kenli Li +3
Matrix factorization (MF) can extract the low-rank features and integrate the information of the data manifold distribution from high-dimensional data, which can consider the nonli…
ComicGAN: Text-to-Comic Generative Adversarial Network
Ben Proven-Bessel, Zilong Zhao, Lydia Chen
Drawing and annotating comic illustrations is a complex and difficult process. No existing machine learning algorithms have been developed to create comic illustrations based on de…
Multi-Label Gold Asymmetric Loss Correction with Single-Label Regulators
Cosmin Octavian Pene, Amirmasoud Ghiassi, Taraneh Younesian +2
Multi-label learning is an emerging extension of the multi-class classification where an image contains multiple labels. Not only acquiring a clean and fully labeled dataset in mul…
DTGAN: Differential Private Training for Tabular GANs
Aditya Kunar, Robert Birke, Zilong Zhao +1
Tabular generative adversarial networks (TGAN) have recently emerged to cater to the need of synthesizing tabular data -- the most widely used data format. While synthetic tabular…
Fed-TGAN: Federated Learning Framework for Synthesizing Tabular Data
Zilong Zhao, Robert Birke, Aditya Kunar +1
Generative Adversarial Networks (GANs) are typically trained to synthesize data, from images and more recently tabular data, under the assumption of directly accessible training da…
Is Shapley Value fair? Improving Client Selection for Mavericks in Federated Learning
Jiyue Huang, Chi Hong, Lydia Y. Chen +1
Shapley Value is commonly adopted to measure and incentivize client participation in federated learning. In this paper, we show -- theoretically and through simulations -- that Sha…
Enhancing Robustness of On-line Learning Models on Highly Noisy Data
Zilong Zhao, Robert Birke, Rui Han +4
Classification algorithms have been widely adopted to detect anomalies for various systems, e.g., IoT, cloud and face recognition, under the common assumption that the data source…
CTAB-GAN: Effective Table Data Synthesizing
Zilong Zhao, Aditya Kunar, Hiek Van der Scheer +2
While data sharing is crucial for knowledge development, privacy concerns and strict regulation (e.g., European General Data Protection Regulation (GDPR)) unfortunately limit its f…