509 citations · 1.1k across the 14 of their papers we have counts for
14 papers
Evolving Connectivity for Recurrent Spiking Neural Networks
Guan Wang, Yuhao Sun, Sijie Cheng +1
Recurrent spiking neural networks (RSNNs) hold great potential for advancing artificial general intelligence, as they draw inspiration from the biological nervous system and show p…
Pairwise Half-graph Discrimination: A Simple Graph-level Self-supervised Strategy for Pre-training Graph Neural Networks
Pengyong Li, Jun Wang, Ziliang Li +6
Self-supervised learning has gradually emerged as a powerful technique for graph representation learning. However, transferable, generalizable, and robust representation learning o…
Contrastive Learning of Subject-Invariant EEG Representations for Cross-Subject Emotion Recognition
Xinke Shen, Xianggen Liu, Xin Hu +2
EEG signals have been reported to be informative and reliable for emotion recognition in recent years. However, the inter-subject variability of emotion-related EEG signals still p…
Learn molecular representations from large-scale unlabeled molecules for drug discovery
Pengyong Li, Jun Wang, Yixuan Qiao +6
How to produce expressive molecular representations is a fundamental challenge in AI-driven drug discovery. Graph neural network (GNN) has emerged as a powerful technique for model…
Pre-training of Graph Neural Network for Modeling Effects of Mutations on Protein-Protein Binding Affinity
Xianggen Liu, Yunan Luo, Sen Song +1
Modeling the effects of mutations on the binding affinity plays a crucial role in protein engineering and drug design. In this study, we develop a novel deep learning based framewo…
Brain-inspired global-local learning incorporated with neuromorphic computing
Yujie Wu, Rong Zhao, Jun Zhu +11
Two main routes of learning methods exist at present including error-driven global learning and neuroscience-oriented local learning. Integrating them into one network may provide…
Unsupervised Paraphrasing by Simulated Annealing
Xianggen Liu, Lili Mou, Fandong Meng +3
Unsupervised paraphrase generation is a promising and important research topic in natural language processing. We propose UPSA, a novel approach that accomplishes Unsupervised Para…
Zooming Network
Yukun Yan, Daqi Zheng, Zhengdong Lu +1
Structural information is important in natural language understanding. Although some current neural net-based models have a limited ability to take local syntactic information, the…
JUMPER: Learning When to Make Classification Decisions in Reading
Xianggen Liu, Lili Mou, Haotian Cui +2
In early years, text classification is typically accomplished by feature-based machine learning models; recently, deep neural networks, as a powerful learning machine, make it poss…
Evaluate the Malignancy of Pulmonary Nodules Using the 3D Deep Leaky Noisy-or Network
Fangzhou Liao, Ming Liang, Zhe Li +2
Automatic diagnosing lung cancer from Computed Tomography (CT) scans involves two steps: detect all suspicious lesions (pulmonary nodules) and evaluate the whole-lung/pulmonary mal…
Event Identification as a Decision Process with Non-linear Representation of Text
Yukun Yan, Daqi Zheng, Zhengdong Lu +1
We propose scale-free Identifier Network(sfIN), a novel model for event identification in documents. In general, sfIN first encodes a document into multi-scale memory stacks, then…
Estimation of the volume of the left ventricle from MRI images using deep neural networks
Fangzhou Liao, Xi Chen, Xiaolin Hu +1
Segmenting human left ventricle (LV) in magnetic resonance imaging (MRI) images and calculating its volume are important for diagnosing cardiac diseases. In 2016, Kaggle organized…
CaMKII activation supports reward-based neural network optimization through Hamiltonian sampling
Zhaofei Yu, David Kappel, Robert Legenstein +3
Synaptic plasticity is implemented and controlled through over thousand different types of molecules in the postsynaptic density and presynaptic boutons that assume a staggering ar…
Attentional Neural Network: Feature Selection Using Cognitive Feedback
Qian Wang, Jiaxing Zhang, Sen Song +1
Attentional Neural Network is a new framework that integrates top-down cognitive bias and bottom-up feature extraction in one coherent architecture. The top-down influence is espec…