Examples of using Multi-task in English and their translations into Chinese
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Programming
Able to handle multi-task work environment.
Know how to apply end-to-end learning, transfer learning, and multi-task learning.
Multi-task learning is a general method for sharing parameters between models that are trained on multiple tasks.
Insufficient virtual memory causes multi-task operation error of the system.
Finally, we also discuss tree-shaped networks, structured prediction,and the prospects of multi-task learning.
Understand how to apply end-to-end learning, multi-task learning, and transfer learning.
Specifically, the weight-training group improved in executive function, which includes the ability to plan,regulate behavior, and multi-task.
If additional data is available, multi-task learning(MTL) can often be used to improve performance on the target task.
All of the previous approaches thus assume that the tasks used in multi-task learning are closely related.
The need for AI-enabled communication, multi-task automation, and analytics solutions will become more prevalent this year.
The 2008 paper by Collobert andWeston proved influential beyond its use of multi-task learning.
From a research perspective, I think that transfer learning and multi-task learning is one of the areas that I would love to figure out.
Finally, we can motivate multi-task learning from a machine learning point of view: We can view multi-task learning as a form of inductive transfer.
From a research perspective, I think that transfer learning and multi-task learning is one of the areas that I would love to figure out.
For example, in multi-task learning, a single model solves multiple tasks, such as a deep model that has different output nodes for different tasks.
Success in today's global market depends on having capable,well-rounded employees who can multi-task to meet a variety of challenges.
Propose a Bayesian neural network for multi-task learning by placing a prior on the model parameters to encourage similar parameters across tasks.
Success in today's business world depends on having capable,well-rounded employees who can multi-task to meet a variety of challenges.
We can motivate multi-task learning in different ways: Biologically, we can see multi-task learning as being inspired by human learning.
In this overview, I have reviewed both the history of literature in multi-task learning as well as more recent work on MTL for Deep Learning.
Users can multi-task with multiple Android apps in moveable windows along with a full desktop browser, all within the familiar Chrome OS interface.
Years to just come, there is a newmessage that foreign do virtual reality multi-task operation software development Envelop VR company closed down.
Viewed through the lens of multi-task learning, a model trained on ImageNet learns a large number of binary classification tasks(one for each class).
Synchrotron radiation beamlines arehigh-performance instruments that allow obtaining multi-scale and multi-task researches on materials of industrial as well as fundamental interest.
The idea of multi-task learning was first proposed in 1993 by Rich Caruana and was applied to road-following and pneumonia prediction(Caruana, 1998).
The Galaxy Tab S delivers an unmatched selection of enhanced productivity and security features that provide users with the ability to effortlessly andsafely multi-task.
Advances in sentiment analysis, question answering, and joint multi-task learning are making it possible for AI to truly understand humans and the way we communicate….
Another related line of work is multi-task learning, where several tasks are learned jointly(Caruna 1993; Augenstein, Vlachos, and Maynard 2015).
In particular, we will discuss two mainideas that have been pervasive throughout the history of multi-task learning: enforcing sparsity across tasks through norm regularization;
Advances in sentiment analysis, question answering, and joint multi-task learning are making it possible for AI to truly understand humans and the way we communicate.