scholar.google.com › citations
Sep 30, 2020 · The main advantage of a parametric technique is the generalization of handling new data, which is particularly beneficial for streaming data ...
Sep 12, 2024 · We found that the training failure comes from the gradient exploding problem, which occurs when data points distant in high-dimensional space ...
Due to minibatch network training, our parametric dimension reduction method is highly efficient. For evaluation, we compared our method to several baselines on ...
People also ask
What is gradient clipping?
Which problem can be handled by gradient clipping method?
Parametric Dimension Reduction by Preserving Local Structure. IEEE VIS ... Facilitate the Parametric Dimension Reduction by Gradient Clipping. CoRR abs ...
Accordingly, we applied the gradient clipping method to solve the problem. Since the networks are trained by directly optimizing the t-SNE objective function, ...
Abstract:It has become standard to use gradient-based dimensionality reduction (DR) methods like tSNE and UMAP when explaining what AI models have learned. This ...
Jun 27, 2023 · In future version, gradient clipping could be included as an option in the loss specification. An example of a parametric t-SNE routine ...
Facilitate the Parametric Dimension Reduction by Gradient Clipping. We extend a well-known dimension reduction method, t-distributed stochas... 0 Chien ...
Nov 4, 2024 · Gradient clipping, a technique designed to prevent the exploding gradient problem by strategically limiting the size of the gradients during the training of ...
Using gradient descent methods, SNE minimizes a KL based cost function. Sampled points from an isotropic Gaussian with small variance centered at the origin ...