Results 51 to 60 of about 639,551 (228)
Classification of Occluded Objects using Fast Recurrent Processing
, 2015 Recurrent neural networks are powerful tools for handling incomplete data problems in computer vision, thanks to their significant generative capabilities.
Yilmaz, Ozgur
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Extreme Learning Machine for Optimized Affine Transformation Based on Gaussian Distribution
Jisuanji kexue yu tansuo, 2021 Extreme learning machine (ELM) is massively mapped to the saturation region of the activation function. Moreover, the input and output of the hidden layer are far from being able to obtain a common distribution method, which gives rise to poor ...
ZHANG Yi, WANG Shitong
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Modeling user navigation [PDF]
, 2003 This paper proposes the use of neural networks as a tool for studying navigation within virtual worlds. Results indicate that the network learned to predict the next step for a given trajectory.
Mangina, E. +4 more
core
Classification via Tensor Decompositions of Echo State Networks
, 2017 This work introduces a tensor-based method to perform supervised classification on spatiotemporal data processed in an echo state network. Typically when performing supervised classification tasks on data processed in an echo state network, the entire ...
Prater, Ashley
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DistilHuBERT: Speech Representation Learning by Layer-wise Distillation of Hidden-unit BERT [PDF]
, 2021 Heng-Jui Chang, Shuwen Yang, Hung-yi Lee
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Toward Pactive (Passive +Active) Sensing for Improved Identification of Material Properties
IEEE Open Journal of Antennas and Propagation, 2023 Subsurface electromagnetic sensing techniques that can measure material properties of hidden layers are useful for applications such as security screening.
Arya Menon, Thomas M. Weller
doaj +1 more source
Sparse Matrix Factorization [PDF]
, 2014 We investigate the problem of factorizing a matrix into several sparse matrices and propose an algorithm for this under randomness and sparsity assumptions.
Neyshabur, Behnam, Panigrahy, Rina
core
Self-Similar Layered Hidden Markov Models [PDF]
, 2001 Hidden Markov Models (HMM) have proven to be useful in a variety of real world applications where considerations for uncertainty are crucial. Such an advantage can be more leveraged if HMM can be scaled up to deal with complex problems. In this paper, we introduce, analyze and demonstrate Self-Similar Layered HMM (SSLHMM), for a certain group of ...
Jafar Adibi, Wei-Min Shen
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IEEE AccessThis study proposes an efficient Artificial Neural Network (ANN) model for predicting the dimensions and feed point of microstrip patch antennas with three types of geometrical shape.
Jitu Prakash Dhar +3 more
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On the ISS Property of the Gradient Flow for Single Hidden-Layer Neural Networks with Linear Activations [PDF]
, 2023 Arthur Castello B. de Oliveira +2 more
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