227 citations · 441 across the 20 of their papers we have counts for
20 papers
Emergence and reconfiguration of modular structure for synaptic neural networks during continual familiarity detection
Shi Gu, Marcelo G Mattar, Huajin Tang +1
While advances in artificial intelligence and neuroscience have enabled the emergence of neural networks capable of learning a wide variety of tasks, our understanding of the tempo…
MindGPT: Interpreting What You See with Non-invasive Brain Recordings
Jiaxuan Chen, Yu Qi, Yueming Wang +1
Decoding of seen visual contents with non-invasive brain recordings has important scientific and practical values. Efforts have been made to recover the seen images from brain sign…
A Human-Machine Joint Learning Framework to Boost Endogenous BCI Training
Hanwen Wang, Yu Qi, Lin Yao +3
Brain-computer interfaces (BCIs) provide a direct pathway from the brain to external devices and have demonstrated great potential for assistive and rehabilitation technologies. En…
Controlling Type Confounding in Ad Hoc Teamwork with Instance-wise Teammate Feedback Rectification
Dong Xing, Pengjie Gu, Qian Zheng +5
Ad hoc teamwork requires an agent to cooperate with unknown teammates without prior coordination. Many works propose to abstract teammate instances into high-level representation o…
Fast-SNN: Fast Spiking Neural Network by Converting Quantized ANN
Yangfan Hu, Qian Zheng, Xudong Jiang +1
Spiking neural networks (SNNs) have shown advantages in computation and energy efficiency over traditional artificial neural networks (ANNs) thanks to their event-driven representa…
DILI: A Distribution-Driven Learned Index (Extended version)
Pengfei Li, Hua Lu, Rong Zhu +3
Targeting in-memory one-dimensional search keys, we propose a novel DIstribution-driven Learned Index tree (DILI), where a concise and computation-efficient linear regression model…
SceneCalib: Automatic Targetless Calibration of Cameras and Lidars in Autonomous Driving
Ayon Sen, Gang Pan, Anton Mitrokhin +1
Accurate camera-to-lidar calibration is a requirement for sensor data fusion in many 3D perception tasks. In this paper, we present SceneCalib, a novel method for simultaneous self…
NeuSort: An Automatic Adaptive Spike Sorting Approach with Neuromorphic Models
Hang Yu, Yu Qi, Gang Pan
Objective. Spike sorting, a critical step in neural data processing, aims to classify spiking events from single electrode recordings based on different waveforms. This study aims…
DANI-Net: Uncalibrated Photometric Stereo by Differentiable Shadow Handling, Anisotropic Reflectance Modeling, and Neural Inverse Rendering
Zongrui Li, Qian Zheng, Boxin Shi +2
Uncalibrated photometric stereo (UPS) is challenging due to the inherent ambiguity brought by the unknown light. Although the ambiguity is alleviated on non-Lambertian objects, the…
Roadmap for Unconventional Computing with Nanotechnology
Giovanni Finocchio, Jean Anne C. Incorvia, Joseph S. Friedman +48
In the "Beyond Moore's Law" era, with increasing edge intelligence, domain-specific computing embracing unconventional approaches will become increasingly prevalent. At the same ti…
Rethinking Sampling Strategies for Unsupervised Person Re-identification
Xumeng Han, Xuehui Yu, Guorong Li +5
Unsupervised person re-identification (re-ID) remains a challenging task. While extensive research has focused on the framework design and loss function, this paper shows that samp…
Policy Optimization with Stochastic Mirror Descent
Long Yang, Yu Zhang, Gang Zheng +5
Improving sample efficiency has been a longstanding goal in reinforcement learning. This paper proposes algorithm: a sample efficient policy gradient method with s…
Monte Carlo Neural Fictitious Self-Play: Approach to Approximate Nash equilibrium of Imperfect-Information Games
Li Zhang, Wei Wang, Shijian Li +1
Researchers on artificial intelligence have achieved human-level intelligence in large-scale perfect-information games, but it is still a challenge to achieve (nearly) optimal resu…
Efficient Spiking Neural Networks with Logarithmic Temporal Coding
Ming Zhang, Nenggan Zheng, De Ma +2
A Spiking Neural Network (SNN) can be trained indirectly by first training an Artificial Neural Network (ANN) with the conventional backpropagation algorithm, then converting it in…
Spiking Deep Residual Network
Yangfan Hu, Huajin Tang, Gang Pan
Spiking neural networks (SNNs) have received significant attention for their biological plausibility. SNNs theoretically have at least the same computational power as traditional a…
Spectral-graph Based Classifications: Linear Regression for Classification and Normalized Radial Basis Function Network
Zhenfang Hu, Gang Pan, Zhaohui Wu
Spectral graph theory has been widely applied in unsupervised and semi-supervised learning. In this paper, we find for the first time, to our knowledge, that it also plays a concre…
Improving Object Detection with Deep Convolutional Networks via Bayesian Optimization and Structured Prediction
Yuting Zhang, Kihyuk Sohn, Ruben Villegas +2
Object detection systems based on the deep convolutional neural network (CNN) have recently made ground- breaking advances on several object detection benchmarks. While the feature…
Robust Face Recognition by Constrained Part-based Alignment
Yuting Zhang, Kui Jia, Yueming Wang +3
Developing a reliable and practical face recognition system is a long-standing goal in computer vision research. Existing literature suggests that pixel-wise face alignment is the…
Sparse Principal Component Analysis via Rotation and Truncation
Zhenfang Hu, Gang Pan, Yueming Wang +1
Sparse principal component analysis (sparse PCA) aims at finding a sparse basis to improve the interpretability over the dense basis of PCA, meanwhile the sparse basis should cover…
Spectral Sparse Representation for Clustering: Evolved from PCA, K-means, Laplacian Eigenmap, and Ratio Cut
Zhenfang Hu, Gang Pan, Yueming Wang +1
Dimensionality reduction, cluster analysis, and sparse representation are basic components in machine learning. However, their relationships have not yet been fully investigated. I…