← Addressing Negative Transfer in Diffusion Models EFHQ: Multi-purpose ExtremePose-Face-HQ dataset → # Fourier Analysis on Robustness of Graph Convolutional Neural Networks for Skeleton-based Action Recognition フーリエ解析を使用して、スケルトンベースのアクション認識のためのグラフ畳み込みニューラル ネットワーク (GCN) の堅牢性と脆弱性を調査します。 グラフ フーリエ変換 (GFT) と離散フーリエ変換 (DFT

nameoverflow What the f__k? Bayesian Neural Networks：贝叶斯神经网络 贝叶斯神经网络，简单来说可以理解为通过为神经网络的权重引入不确定性进行正则化（regularization），也相当于集成（ensemble）某权重分布上的无穷多组神经网络进行预测。 本文主要基于 Charles et al. 2015 [1] 。 另发表于 知乎 。 神经网络的概率模型 众所周知，一个神经网络模型可以视为一个条件分布模型 P ( y ∣ x , w ) P(\mathbf{y

# Neural Network Predictive Modeling / Machine Learning Artificial Neural Network (ANN) is a very powerful predictive modeling technique. Neural network is derived from animal nerve systems (e.g., human brains). The heart of the technique is neural network (or network for short). Neural networks can learn to perform variety of predictive tasks. For example, it can be trained to predict numerical values, say, future stock prices, probability, expected values, etc. In addition, it can be used in tasks that i

NeurIPS Proceedings Search Critical initialisation for deep signal propagation in noisy rectifier neural networks Arnu Pretorius, Elan van Biljon, Steve Kroon, Herman Kamper Advances in Neural Information Processing Systems 31 (NeurIPS 2018) Abstract Stochastic regularisation is an important weapon in the arsenal of a deep learning practitioner. However, despite recent theoretical advances, our understanding of how noise influences signal propagation in deep neural networks remains limited. By extending rec

Convolutional Neural Networks (CNNs) have been proven to be extremely successful at solving computer vision tasks. State-of-the-art methods favor such deep network architectures for its accuracy performance, with the cost of having massive number of parameters and high weights redundancy. Previous works have studied how to prune such CNNs weights. In this paper, we go to another extreme and analyze the performance of a network stacked with a single convolution kernel across layers, as well as other weights

Memory Networks Weston et al. 2015 As with the Neural Turing Machine that we look at yesterday, this paper looks at extending machine learning models with a memory component. The Neural Turing Machine work was developed at Google by the DeepMind team, today's paper on Memory Networks was developed by the Facebook AI Research group

Explore various regularization methods used in neural networks, how they work, and why we need them

Abstract page for arXiv paper 2006.01981: Training End-to-End Analog Neural Networks with Equilibrium Propagation

Many computational properties are maximized when the dynamics of a network are at a 'critical point," a state where systems can quickly change their overall characteristics in fundamental ways, transitioning e.g. between ...

Much of recent machine learning has focused on deep learning, in which neural network weights are trained through variants of stochastic gradient descent. An alternative approach comes from the field of neuroevolution, which harnesses evolutionary algorithms to optimize neural networks, inspired by the fact that natural brains themselves are the products of an evolutionary process. Neuroevolution enables important capabilities that are typically unavailable to gradient-based approaches, including learning n