Automator是蘋果公司為他們的Mac OS X係統開發的一款軟件。隻要通過點擊拖拽鼠標等操作就可以將一係列動作組合成一個工作流,從而幫助你自動的(可重複的)完成一些複雜的工作。Automator還能橫跨很多不同種類的程序,包括:查找器、Safari網絡瀏覽器、iCal、地址簿或者其他的一些程序。它還能和一些第三方的程序一起工作,如微軟的Office、Adobe公司的Photoshop或者Pixelmator等。

熱門內容

Deep learning has penetrated all aspects of our lives and brought us great convenience. However, the process of building a high-quality deep learning system for a specific task is not only time-consuming but also requires lots of resources and relies on human expertise, which hinders the development of deep learning in both industry and academia. To alleviate this problem, a growing number of research projects focus on automated machine learning (AutoML). In this paper, we provide a comprehensive and up-to-date study on the state-of-the-art AutoML. First, we introduce the AutoML techniques in details according to the machine learning pipeline. Then we summarize existing Neural Architecture Search (NAS) research, which is one of the most popular topics in AutoML. We also compare the models generated by NAS algorithms with those human-designed models. Finally, we present several open problems for future research.

0
19
0
下載
預覽

最新內容

Automated software verification of concurrent programs is challenging because of exponentially large state spaces with respect to the number of threads and number of events per thread. Verification techniques such as model checking need to explore a large number of possible executions that are possible under a non-deterministic scheduler. State space reduction techniques such as partial order reduction simplify the verification problem, however, the reduced state space may still be exponentially large and intractable. This paper discusses \emph{Iteratively Relaxed Scheduling}, a framework that uses scheduling constraints in order to simplify the verification problem and enable automated verification of programs which could not be handled with fully non-deterministic scheduling. Program executions are safe as long as the same scheduling constraints are enforced under which the program has been verified, e.g., by instrumenting a program with additional synchronization. As strict enforcement of scheduling constraints may induce a high execution time overhead, we present optimizations over a naive solution that reduce this overhead. Our evaluation of a prototype implementation on well-known benchmark programs shows the effect of scheduling constraints on the execution time overhead and how this overhead can be reduced by relaxing and choosing constraints.

0
0
0
下載
預覽

最新論文

Automated software verification of concurrent programs is challenging because of exponentially large state spaces with respect to the number of threads and number of events per thread. Verification techniques such as model checking need to explore a large number of possible executions that are possible under a non-deterministic scheduler. State space reduction techniques such as partial order reduction simplify the verification problem, however, the reduced state space may still be exponentially large and intractable. This paper discusses \emph{Iteratively Relaxed Scheduling}, a framework that uses scheduling constraints in order to simplify the verification problem and enable automated verification of programs which could not be handled with fully non-deterministic scheduling. Program executions are safe as long as the same scheduling constraints are enforced under which the program has been verified, e.g., by instrumenting a program with additional synchronization. As strict enforcement of scheduling constraints may induce a high execution time overhead, we present optimizations over a naive solution that reduce this overhead. Our evaluation of a prototype implementation on well-known benchmark programs shows the effect of scheduling constraints on the execution time overhead and how this overhead can be reduced by relaxing and choosing constraints.

0
0
0
下載
預覽
Top