Chaos Control on Multi-Branch Universal Learning Network
نویسندگان
چکیده
منابع مشابه
Neural Network Learning Based on Chaos
Chaos and fractals are novel fields of physics and mathematics showing up a new way of universe viewpoint and creating many ideas to solve several present problems. In this paper, a novel algorithm based on the chaotic sequence generator with the highest ability to adapt and reach the global optima is proposed. The adaptive ability of proposal algorithm is flexible in 2 steps. The first one is ...
متن کاملControl of collective network chaos.
Under certain conditions, the collective behavior of a large globally-coupled heterogeneous network of coupled oscillators, as quantified by the macroscopic mean field or order parameter, can exhibit low-dimensional chaotic behavior. Recent advances describe how a small set of "reduced" ordinary differential equations can be derived that captures this mean field behavior. Here, we show that cha...
متن کاملLearning Connections in Multi-branch Residual Networks
Figure 1: Multi-branch residual block (Baseline) [7]. Deep residual learning [3] have led to a series of successful results for image classification. By adopting a residual learning framework, it has become possible to train very deep convolutional neural networks. The degradation problem caused by the increased depth of the network is no longer a serious obstacle for training very deep models ...
متن کاملConnectivity Learning in Multi-Branch Networks
While much of the work in the design of convolutional networks over the last five years has revolved around the empirical investigation of the importance of depth, filter sizes, and number of feature channels, recent studies have shown that branching, i.e., splitting the computation along parallel but distinct threads and then aggregating their outputs, represents a new promising dimension for ...
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: IEEJ Transactions on Electronics, Information and Systems
سال: 1997
ISSN: 0385-4221,1348-8155
DOI: 10.1541/ieejeiss1987.117.3_262