Skip to content Publish AI, ML & data-science insights to a global community of data professionals. Sign in Submit an Article Toggle Mobile Navigation Toggle Search Search Deep Learning Do Vision Transformers See Like Convolutional Neural Networks? (Paper Explained) I will take a closer look at the differences in the obtained representations between CNN and Transformers Akihiro FUJII Oct 9, 2021 18 min read Share Thoughts and Theory Vision Transformer (ViT) has been gaining momentum in recent years. This ar
It is now a standard for neural network representations to be trained on large, publicly available datasets, and used for new problems. The reasons for why neural network representations have been so successful for transfer, however, are still not fully understood. In this paper we show that, after training, neural network representations align their top singular vectors to the targets. We investigate this representation alignment phenomenon in a variety of neural network architectures and find that (a) ali
## Graph-based Dependency Parsing with Graph Neural Networks Tao Ji , Yuanbin Wu , Man Lan We investigate the problem of efficiently incorporating high-order features into neural graph-based dependency parsing. Instead of explicitly extracting high-order features from intermediate parse trees, we develop a more powerful dependency tree node representation which captures high-order information concisely and efficiently. We use graph neural networks (GNNs) to learn the representations and discuss several ne
In the previous article, I was talking about what Neural Networks are and how they are trying to imitate biological neural system. Also, the structure of the neuron, smallest building unit of these networks, was presented. Neurons have this simple structure, and one might say that they alone are useless. Nevertheless, when they are connected with
Transform your payment processes with Neural Payments Hub, enabling seamless, real-time transactions across multiple networks while reducing costs and enhancing user experience
As of April 2023, Uncommon Descent has been archived for historical and research purposes. To stay informed about the latest news and research in the sciences and Intelligent Design, visit Science and Culture Today . ⋮ Learn more Uncommon Descent--> Uncommon Descent Serving The Intelligent Design Community --> Eric Holloway: How AI neural networks show that the mind is not the brain News August 8, 2022 Artificial Intelligence , Intelligent Design , Mind 3 A series of simple diagrams shows that, while AI
Blog Topics Advertise Join Newsletter Neural Networks 201: All About Autoencoders Autoencoders can be a very powerful tool for leveraging unlabeled data to solve a variety of problems, such as learning a "feature extractor" that helps build powerful classifiers, finding anomalies, or doing a Missing Value Imputation. --> comments By Zak Jost , Research Scientist at Amazon Web Services. For those getting started with neural networks, autoencoders can look and sound intimidating. But in fact, they are a conce
Recurrent neural networks & attractor networks give rise to many interesting computational properties, e.g. categorization, filtering noise, integration, memorization. Play with an interactive Hopfield Network and a spiking attractor network
Spiking Neural Networks As Universal Function Approximators
Abstract page for arXiv paper 1506.02626: Learning both Weights and Connections for Efficient Neural Networks