no code implementations • 16 Apr 2024 • Zhichao Zhu, Yang Qi, Wenlian Lu, Zhigang Wang, Lu Cao, Jianfeng Feng
The energy-efficient and brain-like information processing abilities of Spiking Neural Networks (SNNs) have attracted considerable attention, establishing them as a crucial element of brain-inspired computing.
1 code implementation • 13 Mar 2024 • Hengyuan Ma, Wenlian Lu, Jianfeng Feng
Combinatorial optimization problems are widespread but inherently challenging due to their discrete nature. The primary limitation of existing methods is that they can only access a small fraction of the solution space at each iteration, resulting in limited efficiency for searching the global optimal.
1 code implementation • 29 Feb 2024 • Zhikun Xu, Yinghui Li, Ruixue Ding, Xinyu Wang, Boli Chen, Yong Jiang, Hai-Tao Zheng, Wenlian Lu, Pengjun Xie, Fei Huang
To promote the improvement of Chinese LLMs' ability to answer dynamic questions, in this paper, we introduce CDQA, a Chinese Dynamic QA benchmark containing question-answer pairs related to the latest news on the Chinese Internet.
1 code implementation • 1 Jan 2024 • Zhichao Zhu, Yang Qi, Wenlian Lu, Jianfeng Feng
Sensory perception originates from the responses of sensory neurons, which react to a collection of sensory signals linked to various physical attributes of a singular perceptual object.
no code implementations • 25 Aug 2023 • Longbin Zeng, Fengjian Feng, Wenlian Lu
Recent experimental observations have supported the hypothesis that the cerebral cortex operates in a dynamical regime near criticality, where the neuronal network exhibits a mixture of ordered and disordered patterns.
no code implementations • 2 Aug 2023 • Wenlian Lu, Longbin Zeng, Xin Du, Wenyong Zhang, Shitong Xiang, Huarui Wang, Jiexiang Wang, Mingda Ji, Yubo Hou, Minglong Wang, Yuhao Liu, Zhongyu Chen, Qibao Zheng, Ningsheng Xu, Jianfeng Feng
In comparison to most brain simulations with a homogeneous global structure, we highlight that the sparseness, couplingness and heterogeneity in the sMRI, DTI and PET data of the brain has an essential impact on the efficiency of brain simulation, which is proved from the scaling experiments that the DTB of human brain simulation is communication-intensive and memory-access intensive computing systems rather than computation-intensive.
no code implementations • 25 Jul 2023 • Yi Yu, Wenlian Lu, BoYu Chen
We propose theoretical analyses of a modified natural gradient descent method in the neural network function space based on the eigendecompositions of neural tangent kernel and Fisher information matrix.
1 code implementation • 30 May 2023 • Hengyuan Ma, Yang Qi, Li Zhang, Wenlian Lu, Jianfeng Feng
Building robust, interpretable, and secure AI system requires quantifying and representing uncertainty under a probabilistic perspective to mimic human cognitive abilities.
2 code implementations • 23 May 2023 • Yang Qi, Zhichao Zhu, Yiming Wei, Lu Cao, Zhigang Wang, Jie Zhang, Wenlian Lu, Jianfeng Feng
To account for the propagation of correlated neural variability, we derive from first principles a moment embedding for spiking neural network (SNN).
no code implementations • 29 Nov 2022 • Wenlian Lu, Qibao Zheng, Ningsheng Xu, Jianfeng Feng, DTB Consortium
We simulate the human brain at the scale of up to 86 billion neurons, i. e., digital twin brain (DTB), which mimics certain aspects of its biological counterpart both in the resting state and in action.
no code implementations • 2 May 2022 • Xinjia Li, BoYu Chen, Wenlian Lu
The FedDKD introduces a module of decentralized knowledge distillation (DKD) to distill the knowledge of the local models to train the global model by approaching the neural network map average based on the metric of divergence defined in the loss function, other than only averaging parameters as done in literature.
1 code implementation • 2 Jul 2021 • Junya Chen, Zhe Gan, Xuan Li, Qing Guo, Liqun Chen, Shuyang Gao, Tagyoung Chung, Yi Xu, Belinda Zeng, Wenlian Lu, Fan Li, Lawrence Carin, Chenyang Tao
InfoNCE-based contrastive representation learners, such as SimCLR, have been tremendously successful in recent years.
1 code implementation • NeurIPS 2019 • Chenyang Tao, Liqun Chen, Shuyang Dai, Junya Chen, Ke Bai, Dong Wang, Jianfeng Feng, Wenlian Lu, Georgiy Bobashev, Lawrence Carin
Inference, estimation, sampling and likelihood evaluation are four primary goals of probabilistic modeling.
no code implementations • 18 Mar 2019 • Yaoting Huang, Bo-Yu Chen, Wenlian Lu, Zhong-Xiao Jin, Ren Zheng
Due to the complication of the presented problem, intelligent algorithms show great power to solve the parts logistics optimization problem related to the vehicle routing problem (VRP).
1 code implementation • 22 May 2018 • Boyu Chen, Wenlian Lu, Ernest Fokoue
Meta-learning is a promising method to achieve efficient training method towards deep neural net and has been attracting increases interests in recent years.
no code implementations • CVPR 2018 • Changmao Cheng, Yanwei Fu, Yu-Gang Jiang, Wei Liu, Wenlian Lu, Jianfeng Feng, xiangyang xue
Inspired by the recent neuroscience studies on the left-right asymmetry of the human brain in processing low and high spatial frequency information, this paper introduces a dual skipping network which carries out coarse-to-fine object categorization.
no code implementations • 2 Apr 2016 • Wenlian Lu, Ren Zheng, Tianping Chen
In this paper, we discuss the outer-synchronization of the asymmetrically connected recurrent time-varying neural networks.
no code implementations • 2 Apr 2016 • Ren Zheng, Xinlei Yi, Wenlian Lu, Tianping Chen
In this paper, we investigate stability of a class of analytic neural networks with the synaptic feedback via event-triggered rules.