Results 31 to 40 of about 5,846,406 (312)
Evaluation of Deep Learning Models for Multi-Step Ahead Time Series Prediction
IEEE Access, 2021 Time series prediction with neural networks has been the focus of much research in the past few decades. Given the recent deep learning revolution, there has been much attention in using deep learning models for time series prediction, and hence it is ...
Rohitash Chandra +2 more
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Expressivity of Deep Neural Networks
, 2022 This review paper will appear as a book chapter in the book "Theory of Deep Learning" by Cambridge University ...
Ingo Gühring +2 more
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Lazy training of radial basis neural networks [PDF]
, 2006 Proceeding of: 16th International Conference on Artificial Neural Networks, ICANN 2006. Athens, Greece, September 10-14, 2006Usually, training data are not evenly distributed in the input space.
Galván, Inés M. +5 more
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Deep Net Tree Structure for Balance of Capacity and Approximation Ability
Frontiers in Applied Mathematics and Statistics, 2019 Deep learning has been successfully used in various applications including image classification, natural language processing and game theory. The heart of deep learning is to adopt deep neural networks (deep nets for short) with certain structures to ...
Charles K. Chui +4 more
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Deep Sequential Neural Network
CoRR, 2014 Neural Networks sequentially build high-level features through their successive layers. We propose here a new neural network model where each layer is associated with a set of candidate mappings. When an input is processed, at each layer, one mapping among these candidates is selected according to a sequential decision process.
Denoyer, Ludovic, Gallinari, Patrick
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Analyzing the Sensitivity of Deep Neural Networks for Sentiment Analysis: A Scoring Approach
, 2020 Part of IEEE WCCI 2020 is the world’s largest technical event on computational intelligence, featuring the three flagship conferences of the IEEE Computational Intelligence Society (CIS) under one roof: The 2020 International Joint Conference on Neural ...
Wei Emma Zhang +7 more
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On the capacity of deep generative networks for approximating distributions [PDF]
, 2021 We study the efficacy and efficiency of deep generative networks for approximating probability distributions. We prove that neural networks can transform a low-dimensional source distribution to a distribution that is arbitrarily close to a high ...
Wang, Yang, Li, Zhen, Yang, Yunfei
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An Interactive Visualization for Feature Localization in Deep Neural Networks
Frontiers in Artificial Intelligence, 2020 Deep artificial neural networks have become the go-to method for many machine learning tasks. In the field of computer vision, deep convolutional neural networks achieve state-of-the-art performance for tasks such as classification, object detection, or ...
Martin Zurowietz, Tim W. Nattkemper
doaj +1 more source
IEEE Access, 2023 The power system frequency is an important indicator that reflects the power system’s operating status. Through real-time detection or prediction, it can effectively ensure stable power system operation.
Wenzhuo Wang +8 more
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Evolutionary cellular configurations for designing feed-forward neural networks architectures [PDF]
, 2001 Proceeding of: 6th International Work-Conference on Artificial and Natural Neural Networks, IWANN 2001 Granada, Spain, June 13–15, 2001In the recent years, the interest to develop automatic methods to determine appropriate architectures of feed-forward ...
Gutiérrez Sánchez, Germán +6 more
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