If machines can learn, who needs scientists?
نویسندگان
چکیده
منابع مشابه
COVID-19 Control: Can Germany Learn From China?
The coronavirus disease 2019 (COVID-19) outbreak started in China in December 2019 and has developed into a pandemic. Using mandatory large-scale public health interventions including a lockdown with locally varying intensity and duration, China has been successful in controlling the epidemic at an early stage. The epicentre of the pandemic has since shifted to Europe a...
متن کاملPICC Counting: Who Needs Joins When You Can Propagate Efficiently?
Counting is a common task in many data mining applications, including market basket data analysis, scientific inquiry, and other high dimensional data management applications. Given a single table, obtaining the instance counts of the entries in the table is relatively cheap. In situations where the attributes of interest are distributed across different tables, however, the problem of computin...
متن کاملDesigning next-generation platforms for evaluating scientific output: what scientists can learn from the social web
Traditional pre-publication peer review of scientific output is a slow, inefficient, and unreliable process. Efforts to replace or supplement traditional evaluation models with open evaluation platforms that leverage advances in information technology are slowly gaining traction, but remain in the early stages of design and implementation. Here I discuss a number of considerations relevant to t...
متن کاملTell me who can learn you and I can tell you who you are: Landmarking Various Learning Algorithms
Landmarking is a novel approach to describing tasks in meta-learning. Previous approaches to meta-learning mostly considered only statistics-inspired measures of the data as a source for the de nition of metaattributes. Contrary to such approaches, landmarking tries to determine the location of a speci c learning problem in the space of all learning problems by directly measuring the performanc...
متن کاملLearn Ing Class If Ier Systems
Learning Classifier Systems use reinforcement learning, evolutionary computing and/or heuristics to develop adaptive systems. This paper extends the ZCS Learning Classifier System to improve its internal modelling capabilities. Initially, results are presented which show performance in a traditional reinforcement learning task incorporating lookahead within the rule structure. Then a mechanism ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of Magnetic Resonance
سال: 2019
ISSN: 1090-7807
DOI: 10.1016/j.jmr.2019.07.044