Suchergebnisse - multiplication stochastic gradient algorithm

An adaptive Hessian approximated stochastic gradient MCMC method von Wang, Yating, Deng, Wei, Lin, Guang

“… One popular family is stochastic gradient Markov chain Monte Carlo methods (SG-MCMC), which have gained increasing interest due to their ability to handle large datasets and the potential to avoid overfitting …”
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Low-Complexity Feature Stochastic Gradient Algorithm for Block-Lowpass Systems von Yazdanpanah, Hamed, Diniz, Paulo S. R., Lima, Markus V. S.

“… it. By means of the so-called feature function, we propose the low-complexity feature stochastic gradient (LF-SG …”
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An adaptive Hessian approximated stochastic gradient MCMC method von Wang, Yating, Deng, Wei, Lin, Guang

“… One popular family is stochastic gradient Markov chain Monte Carlo methods (SG-MCMC), which have gained increasing interest due to their scalability to handle large datasets and the ability to avoid overfitting …”
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FAST: DNN Training Under Variable Precision Block Floating Point with Stochastic Rounding von Qian Zhang, Sai, McDanel, Bradley, Kung, H. T.

“… In this paper, we propose a Fast First, Accurate Second Training (FAST) system for DNNs, where the weights, activations, and gradients are represented in BFP …”
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Hardware‐Friendly Stochastic and Adaptive Learning in Memristor Convolutional Neural Networks von Zhang, Wei, Pan, Lunshuai, Yan, Xuelong, Zhao, Guangchao, Chen, Hong, Wang, Xingli, Tay, Beng Kang, Zhong, Gaokuo, Li, Jiangyu, Huang, Mingqiang

“… In addition, compared with the traditional nonlinear stochastic gradient descent (SGD) updating algorithm or the piecewise linear …”
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Gradient Descent Using Stochastic Circuits for Efficient Training of Learning Machines von Liu, Siting, Jiang, Honglan, Liu, Leibo, Han, Jie

“… ) and one stochastic integrator are, respectively, used to implement the multiplications and accumulations in a GD algorithm …”
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Data Encoding for Byzantine-Resilient Distributed Optimization von Data, Deepesh, Song, Linqi, Diggavi, Suhas N.

“… : Proximal Gradient Descent (PGD) and Coordinate Descent (CD). Gradient descent (GD) is a special case of these algorithms …”
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Energy-Efficient LSTM Networks for Online Learning von Ergen, Tolga, Mirza, Ali H., Kozat, Suleyman Serdar

“… We then introduce online training algorithms based on the stochastic …”
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A Novel Weld-Seam Defect Detection Algorithm Based on the S-YOLO Model von Zhang, Yi, Ni, Qingjian

“… NAM computes the channel-wise and spatial-wise attention weights by matrix multiplications and element-wise operations, and then applies them to the feature maps …”
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An adaptive Hessian approximated stochastic gradient MCMC method von Wang, Yating, Deng, Wei, Lin, Guang

“… One popular family is stochastic gradient Markov chain Monte Carlo methods (SG-MCMC), which have gained increasing interest due to their ability to handle large datasets and the potential to avoid overfitting …”
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Stochastic Gradient Made Stable: A Manifold Propagation Approach for Large-Scale Optimization von Mu, Yadong, Liu, Wei, Liu, Xiaobai, Fan, Wei

“… To improve the stability of stochastic gradient, recent years have witnessed the proposal of several semi-stochastic gradient descent algorithms, which distinguish themselves from standard SGD …”
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A stochastic learning algorithm for neuromemristive systems von Merkel, Cory, Kudithipudi, Dhireesha

“… Existing algorithms are based on gradient descent techniques, which require analog multiplications …”
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On the Use of Stochastic Hessian Information in Optimization Methods for Machine Learning von Byrd, Richard H., Chin, Gillian M., Neveitt, Will, Nocedal, Jorge

“… We follow a batch approach, also known in the stochastic optimization literature as a sample average approximation approach …”
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The Backpropagation algorithm for a math student von Damadi, Saeed, Moharrer, Golnaz, Cham, Mostafa, Shen, Jinglai

“… The Backpropagation (BP) algorithm leverages the composite structure of the DNN to efficiently compute the gradient …”
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A new constrained optimization model for solving the nonsymmetric stochastic inverse eigenvalue problem von Steidl, Gabriele, Winkler, Maximilian

“… Recently, Zhao et al. [A geometric nonlinear conjugate gradient method for stochastic inverse eigenvalue problems. SIAM J Numer Anal. 2016;54(4):2015-2035 …”
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(\ell_1\) Regression using Lewis Weights Preconditioning and Stochastic Gradient Descent von Durfee, David, Lai, Kevin A, Sawlani, Saurabh

“… We present preconditioned stochastic gradient descent (SGD) algorithms for the \(\ell_1\) minimization problem …”
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Stochastic Matrix-Free Equilibration von Diamond, Steven, Boyd, Stephen

“… Our method is based on convex optimization and projected stochastic gradient descent, using an unbiased estimate of a gradient obtained by a randomized method …”
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Trust-region algorithms for training responses: machine learning methods using indefinite Hessian approximations von Erway, Jennifer B., Griffin, Joshua, Marcia, Roummel F., Omheni, Riadh

“… Methods for solving ML problems based on stochastic gradient descent are easily scaled for very large problems but may involve fine-tuning many hyper-parameters …”
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Digitally Adaptive High-Fidelity Analog Array Signal Processing Resilient to Capacitive Multiplying DAC Inter-Stage Gain Error von Joshi, Siddharth, Kim, Chul, Thomas, Chris M., Cauwenberghs, Gert

“… S 2 A offers a direct alternative to stochastic gradient descent overcoming several of its shortcomings, such as its sensitivity to model error, while improving on the rate and quality of convergence …”
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LM-CMA: An Alternative to L-BFGS for Large-Scale Black Box Optimization von Loshchilov, Ilya

“… ) proposed by Loshchilov ( 2014 ). LM-CMA is a stochastic derivative-free algorithm for numerical optimization of nonlinear, nonconvex …”
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