Notes Seminal blog post demonstrating char-level RNN power. Shakespeare, LaTeX, kernel code generation.

← Unprocessing Seven Years of Algorithmic Fairness Conditional Matrix Flows for Gaussian Graphical Models → # Frozen Overparameterization: A Double Descent Perspective on Transfer Learning of Deep Neural Networks 投稿日: 2023年6月13日 作成者: jarxiv ディープニューラルネットワーク（DNN）の転移学習の一般化動作を研究します。 汎化パフォーマンスに対する転移学習設定の微妙な影響を説明するために、トレーニング データの補間

Abstract page for arXiv paper 2310.04171v1: Dynamic Relation-Attentive Graph Neural Networks for Fraud Detection

# Neural Style Explained The paper A Neural Algorithm of Artistic Style detailed on how to extract two sets of features from a given image: the content, and and the style. In convolutional neural networks, each layer stores information in an abstraction based on the previous layer. For example, the first layer may search for dark pixels in a line to represent an edge. The next layer may then look for two perpendicular edges to represent a corner. The last layer can then return a classification based on wh

Using an LSTM to teach the iPhone how to play the drums

EasyChair Smart Slide User guide Home Title:Vehicle: a High-Level Language for Embedding Logical Specifications in Neural Networks Conference: FoMLAS2022 Tags:Agda, Marabou, Neural networks and Verification Abstract: Verification of neural network specifications is currently an active field of research in automated theorem proving. However, the actual act of verification is merely one step in the process of constructing a verified network. Prior to verification the specification should influence the trainin

Multi-Layer neural networks solve complex, non-linear problems like the spiral dataset

We analyze numerically the training dynamics of deep neural networks (DNN) by using methods developed in statistical physics of glassy systems. The two main issues we address are (1) the complexity

High Resolution Net (HRNet) is a state of the art neural network for human pose estimation - an image processing task which finds the

Brain-inspired spiking neural networks (SNNs) have recently drawn more and more attention due to their event-driven and energy-efficient characteristics. The integration of storage and computation paradigm on neuromorphic hardwares makes SNNs much different from Deep Neural Networks (DNNs). In this paper, we argue that SNNs may not benefit from the weight-sharing mechanism, which can effectively reduce parameters and improve inference efficiency in DNNs, in some hardwares, and assume that an SNN with unshar