← Back to homepage Generative Adversarial Networks, a gentle introduction March 23, 2021 by Chris In the past few years, deep learning has revolutionalized the field of Machine Learning. They are about "discovering rich (...) models" that work well with a variety of data (Goodfellow et al., 2014). While most approaches have been discriminative, over the past few years, we have seen a rise in generative deep learning. Within the field of image generation, Generative Adversarial Networks or GANs have been
The paper introduces Neural GPUs, a novel convolutional GRU model that efficiently learns and generalizes algorithmic tasks, achieving error-free performance on up to 2000-bit inputs
This explores what actually has to be true of a neural circuit before a human can claim to understand it — not just whether it looks tidy, but whether the tidiness maps onto something real about how t
I’ve been trying to apply Sparse Distributed Representations (SDR) to simple neural network layers. Can anyone suggest a method for achieving this? I want to ensure that only 2-5% of the neurons are active in each layer
🤖 Сlear explanation of the term Memory Networks , types, practical used and successful use cases in business
NeurFlow introduces a novel framework that analyzes groups of neurons rather than individual neurons to improve neural network interpretability and reduce computational costs
What are the advantages of ConvNets over FC networks in image analysis? How is ConvNet derived from FC networks? Where the term convolution in CNNs came from? These questions are to be answered in this article
← INFLECT-DGNN: Influencer Prediction with Dynamic Graph Neural Networks Multi-Granularity Framework for Unsupervised Representation Learning of Time Series → # Neural Machine Translation of Clinical Text: An Empirical Investigation into Multilingual Pre-Trained Language Models and Transfer-Learning Transformer ベースの構造などの深層学習を使用した多言語ニューラル ネットワーク モデルを調査することにより
Discover how neural networks build understanding by stacking simple decision makers into layers, just like a fire brigade or a set of sieves
Every so often a new neural network makes headlines for solving a computation problem. It is sometimes hard for me to judge how impressive these achievements