SNR: Sub-Network Routing for Flexible Parameter Sharing in Multi-Task Learning
نویسندگان
چکیده
منابع مشابه
Bayesian Network Parameter Learning using EM with Parameter Sharing
This paper explores the e↵ects of parameter sharing on Bayesian network (BN) parameter learning when there is incomplete data. Using the Expectation Maximization (EM) algorithm, we investigate how varying degrees of parameter sharing, varying number of hidden nodes, and di↵erent dataset sizes impact EM performance. The specific metrics of EM performance examined are: likelihood, error, and the ...
متن کاملFlexible multi-task learning with latent task grouping
In multi-task learning, using task grouping structure has been shown to be effective in preventing inappropriate knowledge transfer among unrelated tasks. However, the group structure often has to be predetermined using prior knowledge or heuristics, which has no theoretical guarantee and could lead to unsatisfactory learning performance. In this paper, we present a flexible multi-task learning...
متن کاملPolicy Model for Sharing Network Slices in 5G Core Network
As mobile data traffic increases, and the number of services provided by the mobile network increases, service load flows as well, which requires changing in the principles, models, and strategies for media transmission streams serving to guarantee the given nature of giving a wide scope of services in Flexible and cost-effective. Right now, the fundamental question remains what number of netwo...
متن کاملA time-dependent vehicle routing problem for disaster response phase in multi-graph-based network
Logistics planning in disaster response phase involves dispatching commodities such as medical materials, personnel, food, etc. to affected areas as soon as possible to accelerate the relief operations. Since transportation vehicles in disaster situations can be considered as scarce resources, thus, the efficient usage of them is substantially important. In this study, we provide a dynamic vehi...
متن کاملMulti-Task Metric Learning on Network Data
Multi-task learning (MTL) has been shown to improve prediction performance in a number of different contexts by learning models jointly on multiple different, but related tasks. Network data, which are a priori data with a rich relational structure, provide an important context for applying MTL. In particular, the explicit relational structure implies that network data is not i.i.d. data. Netwo...
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: Proceedings of the AAAI Conference on Artificial Intelligence
سال: 2019
ISSN: 2374-3468,2159-5399
DOI: 10.1609/aaai.v33i01.3301216