347 citations · 347 across the 7 of their papers we have counts for
7 papers
Efficient Quality-Diversity Optimization through Diverse Quality Species
Ryan Wickman, Bibek Poudel, Michael Villarreal +2
A prevalent limitation of optimizing over a single objective is that it can be misguided, becoming trapped in local optimum. This can be rectified by Quality-Diversity (QD) algorit…
Learning-Based Data Storage [Vision] (Technical Report)
Xiang Lian, Xiaofei Zhang
Deep neural network (DNN) and its variants have been extensively used for a wide spectrum of real applications such as image classification, face/speech recognition, fraud detectio…
A Generic Graph Sparsification Framework using Deep Reinforcement Learning
Ryan Wickman, Xiaofei Zhang, Weizi Li
The interconnectedness and interdependence of modern graphs are growing ever more complex, causing enormous resources for processing, storage, communication, and decision-making of…
One to Multiple Mapping Dual Learning: Learning Multiple Sources from One Mixed Signal
Ting Liu, Wenwu Wang, Xiaofei Zhang +2
Single channel blind source separation (SCBSS) refers to separate multiple sources from a mixed signal collected by a single sensor. The existing methods for SCBSS mainly focus on…
UniFed: A Unified Framework for Federated Learning on Non-IID Image Features
Meirui Jiang, Xiaoxiao Li, Xiaofei Zhang +2
How to tackle non-iid data is a crucial topic in federated learning. This challenging problem not only affects training process, but also harms performance of clients not participa…
End-to-end translation of human neural activity to speech with a dual-dual generative adversarial network
Yina Guo, Xiaofei Zhang, Zhenying Gong +2
In a recent study of auditory evoked potential (AEP) based brain-computer interface (BCI), it was shown that, with an encoder-decoder framework, it is possible to translate human n…
FedBN: Federated Learning on Non-IID Features via Local Batch Normalization
Xiaoxiao Li, Meirui Jiang, Xiaofei Zhang +2
The emerging paradigm of federated learning (FL) strives to enable collaborative training of deep models on the network edge without centrally aggregating raw data and hence improv…