Titlebook: Artificial Neural Networks and Machine Learning – ICANN 2017; 26th International C Alessandra Lintas,Stefano Rovetta,Alessandro E.P. Confe

GNAT

PANG

錯事

https://doi.org/10.1007/978-3-658-30251-1 the substantial characteristics of features extracted from different layers of convolutional neural networks (CNN) are infrequently considered in existing methods. In this paper, we propose a multi-level weighted representation for person re-identification, in which features containing strong discr

neologism

Fernsehen – Internet – Konvergenztate is extremely challenging for several reasons. Each nation-state has usually more than a single cyber unit that develops such advanced malware, rendering traditional authorship attribution algorithms useless. Furthermore, those APTs use state-of-the-art evasion techniques, making feature extract

BOOST

Fernsehen – Internet – Konvergenzeinforcement learning (IRL) is its opposite; given a history of behaviors of an agent, IRL attempts to determine the unknown characteristics, like a reward function, of the agent. Conventional IRL methods usually assume the agent has taken a stationary policy that is optimal in the environment. Howe

召集

Fernsehen – Internet – Konvergenzssuming the .-Bernoulli distribution in each of its hidden units, this result makes it possible to derive cost functions for variants of the DRBM that utilise other distributions, including some that are often encountered in the literature. This paper shows that this function can be extended to the

欄桿

Fernsehen – Internet – Konvergenzhensible, good knowledge representations should be used. So called M of N rules are a compact way of representing knowledge that has a strong intuitive connection to the structure of neural networks. M of N rules have been used in the past in the context of supervised models but not unsupervised mod

epinephrine

Fernsehen – Internet – Konvergenzcal applications ranging from business to medical diagnosis and technical problems. A large number of error functions have been proposed in the literature to achieve a better predictive power. However, only a few works employ Tsallis statistics, which has successfully been applied in other fields. T

新奇

Fortify

Fernsehen – Internet – Konvergenzels. Here we present a novel extension of a previous rule extraction algorithm for RBMs that allows us to quickly extract accurate M of N rules. The results are compared on simple datasets showing that M of N extraction has the potential to be an effective method for the knowledge representation of RBMs.