Interfaces are ubiquitous in Li-ion battery electrodes, occurring across compositional gradients, regions of multiphase intergrowths, and between electrodes and solid electrolyte interphases or protective coatings. However, the impact of these interf...
Training on large-scale graphs has achieved remarkable results in graph representation learning, but its cost and storage have raised growing concerns. As one of the most promising directions, graph condensation methods address these issues by employ...
Federated learning (FL) allows multiple data-owners to collaboratively train machine learning models by exchanging local gradients, while keeping their private data on-device. To simultaneously enhance privacy and training efficiency, recently parame...
In this paper, we introduce a new extended version of the shallow water equations with surface tension which is skew-symmetric with respect to the L2 scalar product and allows for large gradients of fluid height. This result is a generalization of th...
In this paper we investigate the convection phenomenon in the intracluster medium (the weakly-collisional magnetized inhomogeneous plasma permeating galaxy clusters) where the concentration gradient of the Helium ions is not ignorable. To this end,...
The one-loop determination of the coefficient $c_\text{SW}$ of the Wilson quark action has been useful to push the leading cut-off effects for on-shell quantities to $\mathcal{O}(α^2 a)$ and, in conjunction with non-perturbative determinations of $c...
Enlarged Krylov subspace methods and their s-step versions were introduced [7] in the aim of reducing communication when solving systems of linear equations Ax = b. These enlarged CG methods consist of enlarging the Krylov subspace by a maximum of t...
Diffusion MRI (dMRI) provides the ability to reconstruct neuronal fibers in the brain, $\textit{in vivo}$, by measuring water diffusion along angular gradient directions in q-space. High angular resolution diffusion imaging (HARDI) can produce better...
We study stochastic gradient descent (SGD) with local iterations in the presence of malicious/Byzantine clients, motivated by the federated learning. The clients, instead of communicating with the central server in every iteration, maintain their loc...
We study distributed stochastic gradient descent (SGD) in the master-worker architecture under Byzantine attacks. We consider the heterogeneous data model, where different workers may have different local datasets, and we do not make any probabilisti...
We propose a self-contained, resilient and fully distributed solution for locating the maximum of an unknown scalar field using a swarm of robots that travel at a constant speed. Unlike conventional reactive methods relying on gradient information, o...
During embryo development, patterns of protein concentration appear in response to morphogen gradients. These patterns provide spatial and chemical information that directs the fate of the underlying cells. Here, we emulate this process within non-li...
crest was characterized by a detailed and intricate 3D effect, featuring a gradient ball encased within a crescent moon. This emblem also included the full
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away from the gradient that they've sported since the opening of SoFi Stadium in 2020. Overall, the Rams will move away from gradients entirely and incorporate
Learning to store information over extended time intervals by recurrent backpropagation takes a very long time, mostly because of insufficient, decaying error backflow. We briefly review Hochreiter's (1991) analysis of this problem, then address it by introducing a novel, efficie…
As a free CSS gradient generator tool, this website lets you create a colorful gradient background for your website, blog, or social …
Consider a surface whose height above sea level at point (x, y) is H(x, y). The gradient of H at a point is a plane vector pointing in the …
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uiGradients is a handpicked collection of beautiful color gradients for designers and developers.
Aug 21, 2026 · Any slope can be called a gradient. In the interstate highway system, the maximum gradient is 6 percent; in other …
The Gradient Generator page will greet the user with two large color selection panels and a single red slider which will by default be …
Create CSS gradients and explore popular color palettes. Adjust colors, steps, angles, and styles, then copy CSS or export SVG and …
Thousands of trendy color gradients in a curated collection that is updated daily. Get a fresh color gradient for your next design …
Create CSS gradients and explore popular color palettes. Adjust colors, steps, angles, and styles, then copy CSS or export SVG and …
The gradient (also called slope) of a line tells us how steep it is. To find the gradient: Have a play (drag the points):
Gradient descent is a method for unconstrained mathematical optimization. It is a first-order iterative algorithm for minimizing a differentiable multivariate
surface gradient is a vector differential operator that is similar to the conventional gradient. The distinction is that the surface gradient takes effect
Gradient boosting is a machine learning technique based on boosting in a functional space, where the target is pseudo-residuals instead of residuals as
Texture gradient is the distortion in size which closer objects have compared to objects further away. It also involves groups of objects appearing denser
rate. Gradient may also refer to: Gradient sro, a Czech aircraft manufacturer Image gradient, a gradual change or blending of color Color gradient, a range
In hydrodynamics and hydrostatics, the pressure gradient (typically of air but more generally of any fluid) is a physical quantity that describes in which
In machine learning, the vanishing gradient problem is the problem of greatly diverging gradient magnitudes between earlier and later layers encountered
Stochastic gradient descent (often abbreviated SGD) is an iterative method for optimizing an objective function with suitable smoothness properties (e
A temperature gradient is a physical quantity that describes in which direction and at what rate the temperature changes the most rapidly around a particular
everywhere orthogonal to the gradient of the function and that has the same magnitude as the gradient. The skew gradient can be defined using complex
geometry, the four-gradient (or 4-gradient) ∂ {\displaystyle {\boldsymbol {\partial }}} is the four-vector analogue of the gradient ∇ → {\displaystyle
image gradient represents the derivative of an image, so the goal of gradient domain processing is to construct a new image by integrating the gradient, which
In machine learning, backpropagation is a gradient computation method commonly used for training a neural network in computing parameter updates. It is
Gradient-index (GRIN) optics is the branch of optics covering optical effects produced by a gradient of the refractive index of a material. Such gradual