Non-intrusive load monitoring or energy disaggregation involves estimating the power consumption of individual appliances from measurements of the total power consumption of a home. Deep neural networks have been shown to be effective for energy disaggregation. In this work, we present a deep neural network architecture which achieves state of the art disaggregation performance with substantially improved computational efficiency, reducing model training time by a factor of 32 and prediction time by a facto

# 机器学习如何运作 # 机器学习如何运作 How machine learning works ## 文章列表和翻译进度 英文标题 中文标题 进度 How linear regression works 线性回归 Linear Regression 95% How neural networks work 神经网络 Neural Networks Video How backpropagation works 反向传播 Backpropagation Video How deep learning works 深度学习 Deep Learning Video How convolutional neural networks work 卷积神经网络 Convolutional Neural Networks 95% How recurrent neural networks and

Why do some moments imprint themselves in memory while others vanish without a trace? This meta-analysis uncovers a marked dissociation in the brain’s large-scale networks during memory encoding: networks that impede encoding are largely task-invariant, whereas those that support it are finely tuned to the task at hand. Drawing on fMRI studies using the subsequent memory paradigm, the analysis contrasts neural activity during the encoding of later-remembered versus later-forgotten trials across verbal and

Modular Neural Network Performance Tuning: A Definitive Guide Modular neural network performance tuning is the systematic process of optimizing the

Meta-learning neural bloom filters Rae et al., ICML'19 Bloom filters are wonderful things, enabling us to quickly ask whether a given set could possibly contain a certain value. They produce this answer while using minimal space and offering O(1) inserts and lookups. It’s no wonder Bloom filters and their derivatives (the family of approximate set

Category theory can be applied to mathematically model the semantics of cognitive neural systems. We discuss semantics as a hierarchy of concepts, or symbolic descriptions of items sensed and represented in the connection weights distributed throughout a neural network. The hierarchy expresses subconcept relationships, and in a neural network it becomes represented incrementally through a Hebbian-like learning process. The categorical semantic model described here explains the learning process as the deriva

An international team led by scientists at the University of Sydney has demonstrated nanowire networks can exhibit both short- and long-term memory like the human brain

A convolutional neural network (CNN) is a type of deep learning model designed to process grid-like data, such as images

mailitics Networks with Finite VC Dimension: Pro and Contra Networks with Finite VC Dimension: Pro and Contra arXiv:2502.02679v1 Announce Type: new Abstract: Approximation and learning of classifiers of large data sets by neural networks in terms of high-dimensional geometry and statistical learning theory are investigated. The influence of the VC dimension of sets of input-output functions of networks on approximation capabilities is compared with its influence on consistency in learning from samples of da

Neural machine translation exists across a wide variety consumer applications, including web sites, road signs, generating subtitles in foreign languages