A theoretical understanding of generalization remains an open problem for many machine learning models, including deep networks where overparameterization leads to better performance, contradicting the conventional wisdom from classical statistics. Here, we investigate generalization error for kernel regression, which, besides being a popular machine learning method, also describes certain infinitely overparameterized neural networks. We use techniques from statistical mechanics to derive an analytical expr

Community Participants Software Campus Alumni e.V. Communities of Practice Online course Green IT Select Page Projekte » R2-LearN: Reliable Representation Learning for... » R2-LearN: Reliable Representation Learning for Networks Name of the participant: Daniel Zügner Description of the IT-research project: Graph neural networks (GNNs) have transferred the potential of Deep Learning to the graph domain. Because graphs are central to many important applications, GNNs are considered an important class of

Recently I have been teaching myself how to model signal flow in artificial neural networks using R. My personal goal is to understand how proteins in cells and neurons in the brain process information. I am focusing on multilayer perceptrons at the moment

Over the past weeks, I have been witnessing a couple of “self-promoted” AI specialists touting properties that AI does not have (yet). Sure, it may impress people because it looks so magical, but I like facts and reality. Worse, when confronted with real facts, they immediately pretended to be more aware than I am. Of…

Convolutional Neural Networkを使ったスタイル変換手法 Convolutional Neural Network(CNN)を使ったスタイル変換手法として、"A Neural Algorithm of Artistic Style"(以下Neura

Neural Recalibration™ retrains brain response patterns through protocols targeting nerve recalibration and temporal recalibration. MindLAB Neuroscience

For most of the modern marketing era, our “data insight” process has looked something like this: We gather data from various sources. We apply data into existing frameworks and models. We generate insights/conclusions based on patterns we can interpret. We make recommendations based on our perspective. It is a rigorous and time consuming process and…

TensorFlow Neural Style Transfer is an optimization technique used to take two images–a content image and a style image–and blend them together so the output

Recent isotropic networks, such as ConvMixer and vision transformers, have found significant success across visual recognition tasks, matching or outperforming non-isotropic convolutional neural networks (CNNs). Isotropic architectures are particularly well-suited to cross-layer weight sharing, an effective neural network compression technique. In this paper, we perform an empirical evaluation on methods for sharing parameters in isotropic networks (SPIN). We present a framework to formalize major weight sh

A complete guide to Deep Learning; how it works, why it matters, top use cases across sectors, and the future of neural networks in AI