no code implementations • 8 Oct 2024 • Xuming Ran, Juntao Yao, Yusong Wang, Mingkun Xu, Dianbo Liu
In this study, we introduce the Artsy, inspired by the activation mechanisms of silent synapses via spike-timing-dependent plasticity observed in mature brains, to enhance the continual learning capabilities of pre-trained models.
no code implementations • 20 Mar 2024 • Mengyu Yang, Ye Tian, Lanshan Zhang, Xiao Liang, Xuming Ran, Wendong Wang
Recently, prompt-based methods have emerged as a new alternative `parameter-efficient fine-tuning' paradigm, which only fine-tunes a small number of additional parameters while keeping the original model frozen.
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 • 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 • 5 Feb 2021 • Jie Yuan, Xuming Ran, Keyin Liu, Chen Yao, Yi Yao, Haiyan Wu, Quanying Liu
Machine learning is playing an increasingly important role in medical image analysis, spawning new advances in the clinical application of neuroimaging.
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.