20 citations · 43 across the 7 of their papers we have counts for
7 papers
Mugs: A Multi-Granular Self-Supervised Learning Framework
Pan Zhou, Yichen Zhou, Chenyang Si +3
In self-supervised learning, multi-granular features are heavily desired though rarely investigated, as different downstream tasks (e.g., general and fine-grained classification) o…
Adaptive Modeling Against Adversarial Attacks
Zhiwen Yan, Teck Khim Ng
Adversarial training, the process of training a deep learning model with adversarial data, is one of the most successful adversarial defense methods for deep learning models. We ha…
Enhancing Transformation-based Defenses using a Distribution Classifier
Connie Kou, Hwee Kuan Lee, Ee-Chien Chang +1
Adversarial attacks on convolutional neural networks (CNN) have gained significant attention and there have been active research efforts on defense mechanisms. Stochastic input tra…
Theoretical and Experimental Analysis on the Generalizability of Distribution Regression Network
Connie Kou, Hwee Kuan Lee, Jorge Sanz +1
There is emerging interest in performing regression between distributions. In contrast to prediction on single instances, these machine learning methods can be useful for populatio…
PANDA: Facilitating Usable AI Development
Jinyang Gao, Wei Wang, Meihui Zhang +7
Recent advances in artificial intelligence (AI) and machine learning have created a general perception that AI could be used to solve complex problems, and in some situations over-…
Rafiki: Machine Learning as an Analytics Service System
Wei Wang, Sheng Wang, Jinyang Gao +4
Big data analytics is gaining massive momentum in the last few years. Applying machine learning models to big data has become an implicit requirement or an expectation for most ana…
A Compact Network Learning Model for Distribution Regression
Connie Kou, Hwee Kuan Lee, Teck Khim Ng
Despite the superior performance of deep learning in many applications, challenges remain in the area of regression on function spaces. In particular, neural networks are unable to…