Abstract page for arXiv paper 2310.08429v1: Revisiting Data Augmentation for Rotational Invariance in Convolutional Neural Networks

原文地址：Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding

Ever since the work of Minsky and Papert, it has been thought that neural networks derive their effectiveness by finding representations of the data that are

luneur - An implementation of programmable neural networks in Lua

The definition of Neural Network defined and explained in simple language

By taking a look under the hood

It has been observed that graph neural networks (GNN) sometimes struggle to maintain a healthy balance between the efficient modeling long-range dependencies across nodes while avoiding unintended consequences such oversmoothed node representations or sensitivity to spurious edges. To address this issue (among other things), two separate strategies have recently been proposed, namely implicit and unfolded GNNs. The former treats node representations as the fixed points of a deep equilibrium model that can e

Blog Topics Advertise Join Newsletter Training a Neural Network to Write Like Lovecraft In this post, the author attempts to train a neural network to generate Lovecraft-esque prose, known to be awkward and irregular at best. Did it end in success? If not, any suggestions on how it might have? Read on to find out. By Luciano Strika , MercadoLibre on July 11, 2019 in Keras , LSTM , Natural Language Generation , Neural Networks , Python , TensorFlow --> comments LSTM Neural Networks have seen a lot of use in

Machine Learning # Common architectures in convolutional neural networks. #### Jeremy Jordan 19 Apr 2018 • 9 min read In this post, I'll discuss commonly used architectures for convolutional networks. As you'll see, almost all CNN architectures follow the same general design principles of successively applying convolutional layers to the input, periodically downsampling the spatial dimensions while increasing the number of feature maps. While the classic network architectures were comprised simply of s

A Free Lunch From ANN: Towards Efficient, Accurate Spiking Neural Networks CalibrationYuhang Li, Shikuang Deng, Xin Dong, Ruihao Gong, Sh