no code implementations • 26 Apr 2024 • Weiran Chen, Qi Sun, Qi Xu
Spiking Neural Networks (SNNs) aim to bridge the gap between neuroscience and machine learning by emulating the structure of the human nervous system.
no code implementations • 28 Mar 2024 • Qi Xu, Yi Lin, Yunfei Tan, Jianzhao Geng
For the first time, we experimentally demonstrate a self-stable type II superconducting maglev system which is able to: counteract long term levitation force decay, adjust levitation force and equilibrium position, and establish levitation under zero field cooling condition.
no code implementations • 4 Mar 2024 • Yudi Zhang, Qi Xu, Lei Zhang
Creating 3D textured meshes using generative artificial intelligence has garnered significant attention recently.
no code implementations • 27 Jan 2024 • Zhaoyang Qu, Yunchang Dong, Yang Li, Siqi Song, Tao Jiang, Min Li, Qiming Wang, Lei Wang, Xiaoyong Bo, Jiye Zang, Qi Xu
Unfortunately, this approach tends to overlook the inherent topological correlations within the non-Euclidean spatial attributes of power grid data, consequently leading to diminished accuracy in attack localization.
2 code implementations • 11 Jan 2024 • Zhaowei Li, Qi Xu, Dong Zhang, Hang Song, Yiqing Cai, Qi Qi, Ran Zhou, Junting Pan, Zefeng Li, Van Tu Vu, Zhida Huang, Tao Wang
Beyond capturing global information like other multi-modal models, our proposed model excels at tasks demanding a detailed understanding of local information within the input.
no code implementations • NeurIPS 2023 • Qi Xu, Yuyuan Gao, Jiangrong Shen, Yaxin Li, Xuming Ran, Huajin Tang, Gang Pan
Spiking neural networks (SNNs) serve as one type of efficient model to process spatio-temporal patterns in time series, such as the Address-Event Representation data collected from Dynamic Vision Sensor (DVS).
no code implementations • 22 Dec 2023 • Qi Xu, Lijie Wang, Jing Wang, Song Chen, Lin Cheng, Yi Kang
In recent years, analog circuits have received extensive attention and are widely used in many emerging applications.
no code implementations • 21 Nov 2023 • Jiuchen Zhang, Fei Xue, Qi Xu, Jung-Ah Lee, Annie Qu
In this paper, we propose an individualized dynamic latent factor model for irregular multi-resolution time series data to interpolate unsampled measurements of time series with low resolution.
no code implementations • 6 Jun 2023 • Jiangrong Shen, Qi Xu, Jian K. Liu, Yueming Wang, Gang Pan, Huajin Tang
To take full advantage of low power consumption and improve the efficiency of these models further, the pruning methods have been explored to find sparse SNNs without redundancy connections after training.
no code implementations • 30 May 2023 • Junpeng Wang, Mengke Ge, Bo Ding, Qi Xu, Song Chen, Yi Kang
As one of the feasible processing-in-memory(PIM) architectures, 3D-stacked-DRAM-based PIM(DRAM-PIM) architecture enables large-capacity memory and low-cost memory access, which is a promising solution for DNN accelerators with better performance and energy efficiency.
no code implementations • 27 Apr 2023 • Pedro H. C. Sant'Anna, Qi Xu
Additionally, we document a trade-off related to compositional changes: We derive the asymptotic bias of DR DiD estimators that erroneously exclude compositional changes and the efficiency loss when one fails to correctly rule out compositional changes.
no code implementations • 19 Apr 2023 • Qi Xu, Yaxin Li, Xuanye Fang, Jiangrong Shen, Jian K. Liu, Huajin Tang, Gang Pan
The proposed method explores a novel dynamical way for structure learning from scratch in SNNs which could build a bridge to close the gap between deep learning and bio-inspired neural dynamics.
no code implementations • CVPR 2023 • Qi Xu, Yaxin Li, Jiangrong Shen, Jian K Liu, Huajin Tang, Gang Pan
Spiking neural networks (SNNs) are well known as the brain-inspired models with high computing efficiency, due to a key component that they utilize spikes as information units, close to the biological neural systems.
no code implementations • 11 Dec 2022 • Bo Ding, Jinglei Huang, Junpeng Wang, Qi Xu, Song Chen, Yi Kang
To better solve the problems in the automation process of FPGA-PDRS and narrow the gap between algorithm and application, in this paper, we propose a complete workflow including three parts, pre-processing to generate the list of task modules candidate shapes according to the resources requirements, exploration process to search the solution of task modules partitioning, scheduling, and floorplanning, and post-optimization to improve the success rate of floorplan.
no code implementations • 30 Nov 2021 • Xuming Ran, Jie Zhang, Ziyuan Ye, Haiyan Wu, Qi Xu, Huihui Zhou, Quanying Liu
In this study, we propose an integrated framework called Deep Autoencoder with Neural Response (DAE-NR), which incorporates information from ANN and the visual cortex to achieve better image reconstruction performance and higher neural representation similarity between biological and artificial neurons.
no code implementations • 20 May 2021 • Atsushi Inoue, Tong Li, Qi Xu
This paper proposes a new framework to evaluate unconditional quantile effects (UQE) in a data combination model.
no code implementations • 5 Oct 2020 • Xuming Ran, Mingkun Xu, Qi Xu, Huihui Zhou, Quanying Liu
The likelihood-based generative models have been reported to be highly robust to the out-of-distribution (OOD) inputs and can be a detector by assuming that the model assigns higher likelihoods to the samples from the in-distribution (ID) dataset than an OOD dataset.
no code implementations • 16 Jul 2020 • Xuming Ran, Mingkun Xu, Lingrui Mei, Qi Xu, Quanying Liu
To address this problem, a reliable uncertainty estimation is considered to be critical for in-depth understanding of OOD inputs.
2 code implementations • CVPR 2020 • Chengying Gao, Qi Liu, Qi Xu, Li-Min Wang, Jianzhuang Liu, Changqing Zou
We introduce the first method for automatic image generation from scene-level freehand sketches.
Ranked #2 on Sketch-to-Image Translation on SketchyCOCO
no code implementations • 19 Nov 2019 • Qianhui Liu, Gang Pan, Haibo Ruan, Dong Xing, Qi Xu, Huajin Tang
This paper proposes an unsupervised address event representation (AER) object recognition approach.