PDF Ebook about: Neural Networks
Neural Networks
List of ebooks and manuals about Neural Networks
50 documents available in our comprehensive collection of Neural Networks resources. Find practical guides, tutorials, and documentation to enhance your knowledge.
Recurrent Neural Networks (3y0W8O8qSJptHUVFG9nPCb5HU4c.pdf)
210 KBEN 2 pages
The author discusses their experience at the International Conference on Machine Learning (ICML) and introduces two research studies on Recurrent Neural
Graph Neural Networks (GNNs_for_Track_Finding.pdf)
2.81 MBEN 39 pages
The Exa.TrkX project aims to optimize ML approaches for the Exascale tracking problem, enabling production-level tracking on next-generation detector systems.
Neural Network Game (1dbbd6_4243cb08c59348b790ea0c982e2f6a6c.pdf)
3.9 MBEN 7 pages
The Contour to Classification game teaches middle school students basic concepts in supervised learning through an online variant of the Neural Network game,
On Neural Networks with Minimal Weights (etr005.pdf)
137 KBES 7 pages
Un texto críptico que combina letras, números y símbolos, posiblemente codificando información secreta o un mensaje enigmático.
Neural Network Compression (1701.04923.pdf)
614 KBEN 10 pages
Researchers studied compressing deep neural networks for image instance retrieval, focusing on quantization, coding, pruning, and weight sharing techniques to
Benefits of depth in neural networks (telgarsky16.pdf)
320 KBEN 23 pages
Specifically, it shows that for any positive integer k, there exist neural networks with Θ(k3) layers, Θ(1) nodes per layer, and Θ(1) distinct parameters that
Neural Networks Computations with DOMINATION Functions (etr151.pdf)
250 KBEN 6 pages
Researchers Kordag Mehmet Kilic and Jehoshua Bruck propose a novel neural network representation using DOMINATION functions, demonstrated through unweighted bipartite graphs connected to universal gates
“Neural Architecture Search with reinforcement learning”(2017) 논문 (1611.01578.pdf)
716 KBEN 16 pages
Neural Architecture Search uses a recurrent network to generate model descriptions of neural networks and trains this RNN with reinforcement learning to
Practical Diagnostic Tools for Deep Neural Networks (casper_sm_thesis-4.pdf)
15.73 MBEN 101 pages
This thesis proposes practical diagnostic tools for deep neural networks, focusing on evaluating techniques based on their usefulness for identifying problems
Removing lavalier microphone rustle with recurrent neural networks (WichernLukin18.pdf)
746 KBEN 7 pages
Researchers from iZotope, Inc.
An Improved Analysis of TrainingOver-parameterized Deep Neural Networks (8479-an-improved-analysis-of-training-over-parameterized-deep-neural-networks.pdf)
491 KBEN 10 pages
The authors provide a milder over-parameterization condition and faster global convergence rates than previous work.
Filling in the Gap: a General Method Using Neural Networks (0453.pdf)
88 KBEN 4 pages
A general method using neural networks to fill in gaps in medical signals is presented
William Gilpin; Cellular automata as convolutional neural networks (1809.02942.pdf)
4.51 MBEN 12 pages
Deep learning techniques have demonstrated success in predicting complex dynamical systems, motivating questions about how neural networks encode and represent
Passive Exposure Enhances Categorization Learning in Mice and Neural Networks (88406-v1.pdf)
1.54 MBEN 30 pages
Passive exposure to task-relevant stimuli accelerates categorization learning in mice, demonstrating the potential for efficient learning through effortless sensory stimulation.
CRF-RNN Model (Zheng_Conditional_Random_Fields_ICCV_2015_paper.pdf)
1010 KBEN 9 pages
Conditional Random Fields are formulated as Recurrent Neural Networks to combine the strengths of Convolutional Neural Networks and CRFs for pixel-level
Modern Neural Networks Generalize Well on Small Data Sets (7620-modern-neural-networks-generalize-on-small-data-sets.pdf)
992 KBEN 10 pages
Researchers from the University of Pennsylvania found that large neural networks can generalize well on small, noisy data sets, even with hundreds of
Optimizing Functionals on the Space of Probabilities with Input Convex Neural Networks (2106.00774.pdf)
5.64 MBEN 32 pages
We propose using input-convex neural networks to approximate the scheme
Neural Design Network: Graphic Layout Generation with Constraints (1912.09421.pdf)
1.49 MBEN 16 pages
Researchers propose a Neural Design Network (NDN) for generating graphic design layouts that satisfy user-specified constraints
https://arxiv.org/pdf/1609.03552v2.pdf (1609.03552v2.pdf)
6.25 MBEN 16 pages
Researchers propose a method for realistic image manipulation using a generative adversarial neural network to learn the natural image manifold, allowing for
Self-Learnable Activation Functions: Revolutionizing Neural Network Understanding (1906.09529.pdf)
448 KBEN 18 pages
Document en en
The use of artificial neural networks to diagnose Alzheimer_s disease from brain images (Fouladi-The use of artificial neural networks.pdf)
2.96 MBEN 41 pages
Researchers investigated the use of artificial neural networks (ANNs) to diagnose Alzheimer's disease (AD) from brain images of subjects with mild cognitive
Drought Forecasting Models (2018022828.pdf)
799 KBEN 9 pages
Drought is predicted using neuro-fuzzy adaptive inference systems (ANFIS), artificial neural network of multilayered perceptron (ANN-MLP), and support vector
http://ceur-ws.org/Vol-2473/paper4.pdf (paper4.pdf)
429 KBEN 10 pages
Rules extraction from neural networks trained on multimedia data is explored, focusing on methods that extract logical rules from trained networks
AlphaGo (ai45-handout4.pdf)
340 KBEN 7 pages
AlphaGo uses Monte-Carlo Tree Search (MCTS) with neural networks to play Go
https://www.eurasip.org/Proceedings/Eusipco/Eusipco2020/pdfs/0001427.pdf (0001427.pdf)
2.27 MBEN 5 pages
Neural network models are used in various applications, but their decisions are often nontransparent
Efficient Identification and Inversion of Complex-Valued Wiener Systems Using B-Spline Neural Networks (ijcnn2012-id4.pdf)
3.9 MBEN 8 pages
The paper presents a complex-valued (CV) B-spline neural network method for identifying and inverting CV Wiener systems
Deep Learning Optimization (03_775-788_00920_Bpast.No_.66-6_31.12.18_K2.pdf)
5.43 MBEN 13 pages
The landscape of empirical risk of overparametrized deep convolutional neural networks (DCNNs) is characterized by a large number of global minimizers with
pdf (30-CR.pdf)
498 KBEN 14 pages
The team experimented with two classification techniques: convolutional neural networks (CNN) and Fisher vectors-based discriminant models
Neonatal Seizure Detection (CNN_seizure.pdf)
1.39 MBEN 20 pages
Researchers used deep convolutional neural networks (CNNs) and random forest to automatically optimize feature selection and classification for neonatal
http://speech.ee.ntu.edu.tw/~tlkagk/courses/ML_2019/Lecture/Meta2%20(v4).pdf (Meta2 v4.pdf)
1.82 MBEN 30 pages
Gradient Descent can be viewed as a Long Short-Term Memory (LSTM) network, where the learning algorithm resembles a Recurrent Neural Network (RNN) and the
КОНЦЕПТУАЛЬНЫЕ АСПЕКТЫ ПОСТРОЕНИЯ ДОВЕРЕННЫХ НЕОДНОРОДНЫХ БЛОКЧЕЙН-СРЕД НОВОГО ТЕХНОЛОГИЧЕСКОГО УКЛАДА (konceptualnie_aspekti_postroeniya_doverennih_neodnorodnih_blokcheyn-sred_novogo_tehnologicheskogo_uklada.pdf)
343 KBEN 6 pages
The transition to a new technological structure involves the joint use of technologies such as blockchain, IoT, neural networks, and cloud platforms to create
Slides (baydin-2019-neurips-slides.pdf)
7.65 MBEN 67 pages
Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model, involving probabilistic programming, deep learning, and neural networks,
DeepPINK (8085-deeppink-reproducible-feature-selection-in-deep-neural-networks.pdf)
1.12 MBEN 11 pages
DeepPINK is a method for reproducible feature selection in deep neural networks, incorporating the idea of feature selection with controlled error rate by
Green Version (RJ-2017-016.pdf)
822 KBEN 16 pages
Complex nonparametric models like neural networks and random forests are common in predictive analytics, but understanding their results can be challenging
Link to Full Text (8b7dc6e8b36bcaa_ek.pdf)
96 KBEN 5 pages
An off-line signature recognition and verification system is presented, using a moment invariant method and Artificial Neural Networks (ANN)
Neural Task Graphs (Huang_Neural_Task_Graphs_Generalizing_to_Unseen_Tasks_From_a_Single_CVPR_2019_paper.pdf)
1.78 MBEN 10 pages
Neural Task Graph (NTG) Networks are proposed to generalize to unseen tasks from a single video demonstration, incorporating compositionality into the model to
Download PDF (wang22d.pdf)
817 KBEN 12 pages
Adversarially Robust Imitation Learning (ARIL) is proposed to address the vulnerability of deep neural networks (DNNs) to subtle noise in imitation learning
Quantum Surrogate Modeling (2306.05042v1.pdf)
250 KBEN 7 pages
Researchers from LMU Munich and industry partners demonstrate the potential of quantum neural networks (QNNs) as surrogate models for chemical and
Green Version (isprs-archives-XLII-3-W8-1-2019.pdf)
967 KBEN 7 pages
This study proposes a deep learning approach for detecting chestnut clusters using Convolutional Neural Networks (CNNs), specifically the Xception
Optimum Dome Design (---.pdf)
432 KBEN 15 pages
Optimum Design (Minimum Weight) of Sigle-layer-lattice Domes with Different Topologies using Genetic Algorithm and Artificial Neural Networks
Invertible Interpretation Network (paper.pdf)
13.07 MBEN 19 pages
Neural networks learn powerful representations, but their hidden layers lack interpretability due to distributed coding, making it hard to attribute meaning to
https://arxiv.org/pdf/1911.13299.pdf (1911.13299.pdf)
1.01 MBEN 13 pages
Researchers demonstrate that randomly weighted neural networks contain subnetworks that can achieve impressive performance without modifying the weight values
proof-of-concept study (2208.02235.pdf)
1.97 MBEN 14 pages
Researchers propose Quantum-Inspired Tensor Neural Networks (TNN) to solve Partial Differential Equations (PDE), overcoming limitations of traditional Deep
http://arxiv.org/pdf/1404.7828v4.pdf (1404.7828v4.pdf)
1.11 MBEN 88 pages
Deep artificial neural networks have won numerous contests in pattern recognition and machine learning, with deep learning distinguished by the depth of credit
Full-text (DeepLearningInNeuralNetworksOverview.JSchmidhuber2015.pdf)
839 KBEN 33 pages
Deep artificial neural networks have won numerous contests in pattern recognition and machine learning, with deep supervised learning, unsupervised learning,
Simultaneous Speech Recognition (201612_SLT_Sakti_1.paper.pdf)
481 KBEN 8 pages
Researchers propose a method for simultaneous recognition of speech and environmental sounds using deep neural networks (DNNs), combining bottleneck features
Fourier CNN Forecasting (ds2019.pdf)
283 KBEN 10 pages
Researchers propose a time series forecasting framework using Convolutional Neural Networks (CNNs) that integrates required preprocessing steps and makes no
Full paper (1706.04313.pdf)
9.43 MBEN 10 pages
Convolutional neural networks (CNNs) have shown great success in computer vision, but typically do not exhibit compositionality, where the representation of
Joule Counting Correction (8742-13-12270-1-2-20201228.pdf)
1.13 MBEN 8 pages
A method for estimating battery remaining energy in electric vehicles using artificial neural networks is demonstrated, achieving accuracy within 2
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