Sentence examples for net training from inspiring English sources

For instance, many training firms may find training profitable even without subsidies, while other non-training firms may expect a net training investment even with training subsidies in place (and thus will not alter their training behaviour).

As is now well-known, we lack any theory that proves realistic bounds on the convergence time or sample requirements of such deep net training.

This article is about Generative Adversarial Nets (GANs), a proposal by Goodfellow et al. in 20141 to solve this task by harnessing the power of large-scale deep learning (sometimes also called neural net training).

Our paper above generated some discussions with colleagues, who suggested that our result should be perhaps viewed as saying "only" that GANs training has ill-behaved solutions, which should not be surprising since so does usual deep net training.

You can feed the text of Wikipedia, many billions of words long, into a simple neural net, training it to spit out, for each word, a big list of numbers that correspond to the excitement of each neuron in a layer.

(This training is completely analogous to standard deep net training, where a deep net could, for instance, be trained to distinguish between pictures of cats and humans). While such a discriminator is being trained, the generator deep net is trained alongside to produce outputs that cause the discriminator to output a high value.

A Deep Neural Net Trained for Person Categorization Develops Both Detailed Local Features and Broad Contexual Specificities.

Y Combinator has even begun using an A.I. bot, Hal9000, to help it sift admission applications: the bot's neural net trains itself by assessing previous applications and those companies' outcomes.

It is shown that the neural net trained with data from the work described in this paper could predict data of other authors as well, a generality which is not inherent in any of the mass transfer correlations proposed for mass transfer in two-phase systems to date.

Second, we will enhance our approach with a second net trained to recover parameters that determine a sampling distribution over beliefs and reward functions (i.e. producing something akin to a salience map for high-probability locations where an agent's rewards may be and types of assumptions the agent may be acting under).

Can a neural net trained on our experiences, and emotional responses to them, be able to create audio stimulation that would invoke a desired sensation?