Visualize gradient descent optimization algorithms in Tensorflow. All methods start at the same location, specified by two variables. Both x and y variables are improved by the following Optimizers: ...

But what if this ability wasn’t limited to a select few? What if visualization is the key to unlocking exceptional success for anyone willing to harness it? What if it is the secret power of ...

In order to obtain more stable solutions, a distributed approximate gradient descent optimization algorithm and conflict resolution mechanism are proposed, which enhances the convergence of our method ...

Minimized dummy Cost Function f(x) = x^2 using default values as initial approximation = 1, error tolerance = 0.0001, learning rate = 0.1, gamma = 0.9, beta_1 = 0.9 ...

To detect OOD images and classify ID samples, prior methods have been proposed by regarding the background regions of ID samples as the OOD knowledge and performing OOD regularization and ID ...

Guangdong Provincial Key Laboratory of Nanophotonic Manipulation, Institute of Nanophotonics, College of Physics and Optoelectronic Engineering, Jinan University, Guangzhou 511443, China ...