论文笔记:NeurIPS'19 Understanding the Representation Power of Graph Neural Networks 天下客 机器学习、联邦学习、图神经网络6 人赞同了该文章 前言 本文探讨在学习图拓扑方面 GNN 的表征能力,发现 GCN 在学习图矩方面具有局限性,因此作者从理论上分析了 GCN 的表征能力发现使用不同传播规则和残差连接可以显著...
Representation Power of Neural NetworksMatus Telgarsky
including non-smooth functions. We find that having different graph propagation rules with residual connections can dramatically increase the representation power of GCNs.
(OSU) 讲座题目:Reward-free RL via Sample-Efficient Representation Learning 讲座摘要:As reward-free reinforcement learning (RL) becomes a powerful framework for a variety of multi-objective applications, representation learning arises as an effective technique to deal with the curse of dimensionality in...
Minsky, who in 1951 built the first neuro-computer, pointed out in his famous book on computation (Minsky, 1967), the neuronal networks of McCulloch and Pitts have the same computational power as finite-state machines (Perrin, 1990). These observations established an interesting connection ...
The recently developed variational autoencoders (VAEs) have proved to be an effective confluence of the rich representational power of neural networks with Bayesian methods. However, most work on VAEs use a rather simple prior over the latent variables such as standard normal distribution, thereby...
of points and detecting the presence of a surface in between (Ye et al.,2022). These hybrid representations try to increase the representation power at the cost of added complexity. However, this means that the representation capacity of the neural networks are spent on learning additional ...
Brain-inspired computer architectures have gained a lot of attention from computer scientists in the past few years, in an era where more improvements in performance, power, and scalability are increasingly becoming limited. In this paper, we develop a brain-computer structural metaphor where we rai...
Results obtained from experiments with neural networks on Italian data are highly consistent with the body of knowledge derived from previous classical analysis. The explicative power of neural network models proved to be higher than that of path analysis given their capacity to uncover any kind or...
Visualization and analysis of voltage stability using self-organizing neural networks On the basis of a compelling mathematical description of voltage stability in electrical power systems and its indication using the minimum singular value of the load flow Jacobian the application of a self-organizing ...