[PDF] Top 20 A Method For Generation Of High-Nonlinear S-Boxes Based On Gradient Descent
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A Method For Generation Of High-Nonlinear S-Boxes Based On Gradient Descent
... This problem is partially solved by involving the classes of vectorial Boolean functions, extended affine (EA) and Carlet-Charpin-Zinoviev (CCZ) equivalencies [1, 5]. However, the majority of existent functions have ex- ... See full document
9
A New Rule-weight Learning Method based on Gradient Descent
... rule generation, in ...rule generation, ...a method of rule weight learning based on ROC analysis, while the second follows a heuristic weighting ... See full document
6
SAR Images Co registration Based on Gradient Descent Optimization
... Feature based methods are based on the extraction of the same features on the two images to be ...with high curvature), Lines (region boundaries, coastlines, roads, rivers), Regions (forests, lakes, ... See full document
7
A DESCENT PRP CONJUGATE GRADIENT METHOD FOR UNCONSTRAINED OPTIMIZATION
... PRP method for general nonlinear function is uncertain, Powell’s example shows that when the function is not strongly convex, the PRP method may not converge even with an exact line ...search. ... See full document
14
ANN based method for improving gold price forecasting accuracy through modified gradient descent methods
... ANN based method for improving gold price forecasting accuracy ...methods based on application of AI are inherently capable of making suitable ...and nonlinear relationships without any prior ... See full document
12
Constrained Search for a Class of Good S-Boxes with Improved DPA Resistivity
... of S-box F to DPA attacks. Also Prouff showed that S-boxes with very high nonlinearity and those which satisfy propagation criteria (PC) of higher order are more susceptible to DPA ... See full document
19
The modified accelerated Bregman method for regularized basis pursuit problem
... iteration method was presented in [], which was motivated by ...Bregman method is to combine a fixed point iteration with the Bregman method in [, ], but the linearized Bregman method is ... See full document
17
Fast Online Training with Frequency Adaptive Learning Rates for Chinese Word Segmentation and New Word Detection
... present high dimensional new features, including word-based features and enriched edge (label-transition) features, for the joint ...adding high dimensional new features will further slow down the ... See full document
10
Cellular Automata Based S-boxes
... stitution Boxes (S-boxes) having good cryptographic properties and low im- plementation ...χ nonlinear transformation used in ...of S-boxes defined by CA, proving some upper ... See full document
26
A New Nonlinear Conjugate Gradient Method Based on the Scaled Matrix
... In this paper, we have proposed new a nonlinear CG- algorithms based on the Scaled Matrix defined by (17) under some assumptions. The new algorithm has been shown to be globally convergent and satisfies the ... See full document
6
A Comparative Study of Nonlinear Time Varying Process Modeling Techniques: Application to Chemical Reactor
... is based on supervised methods or algorithms using direct time-consuming cal- culation to determine the minimum of ...(the Gradient Descent method) and evolutionary al- gorithms (Genetic ... See full document
9
A Compression Method for PML Document Based on Internet of Things
... Obviously the Lossy compression is not out cake because the PML data such as EPC tag needs to be accurate to identify the object after decompression. Among the lossless compression techniques like LZW (Lempel-Ziv-Welch) ... See full document
6
Multi Valued Neuron with Sigmoid Activation Function for Pattern Classification
... z is useful when learning non-linear functions with a number of high irregular jumps. Equation (3) ensures that the corrected weighted sum moves from sector s to sector q (see Figure 2(a)). The direction of ... See full document
10
Comfort Improvement of a Novel Nonlinear Suspension for a Seat System Based on Field Measurements
... novel nonlinear suspension for seat system based on field ...novel nonlinear suspension proposed, a rubber spring is used as its elastic element which has highly nonlinear characteristics to ... See full document
9
Hybrid Optimized Back propagation Learning Algorithm for Multi layer Perceptron
... Two modifications to the classical approach of the Quasi- Newton method have been presented. It was shown that the hypotheses supporting those methods are relevant and desirable in terms of convergence properties. ... See full document
5
Machine Learning from a Continuous Viewpoint. Weinan E. Princeton University
... We will see that gradient descent algorithm (GD) for random feature and neural network models are simply the particle method discretization of the gradient flows discussed before..?. “co[r] ... See full document
31
Certain Systems Arising In Stochastic Gradient Descent
... Here, we will discuss the ODE-method for a more detailed exposition, see [KY03], which is used to establish convergence results for stochastic approximations. This method links the asymptotic behavior of ... See full document
105
Optimal control based on nonlinear conjugate gradient method in cardiac electrophysiology
... hybrid nonlinear conjugate gradient ...hybrid nonlinear conjugate gradient method is developed in this research for solving ...developed method was proven to be performed better ... See full document
46
Testing and non-linear preconditioning of the proximal point method
... that in a stochastic setting, we only need the expectation E[ ∆ i+1 ] in Corollary 4.7 and Theorem 4.6. Therefore, we can relax the relevant condition (CI ∼ ), (CI), (CI- Γ ), or (CI- G ) to the expectation. This may ... See full document
31
Detection of Mobile Keyloggers Using Deep Learning
... Gradient descent is an algorithm of first order iterative optimization to find the minimum of a ...function's gradient with respect to the parameters where the gradient gives the direction of ... See full document
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