NFC

NFC This pages is designed to illuminate what will be 5G towards 6G networks. Firstly, we mainly focus on applications of AI and DL for wireless networks.

Good source for most popular papers on MLhttp://www.arxiv-sanity.com/Paper title: Automatic Prediction of Building Age f...
23/04/2018

Good source for most popular papers on ML
http://www.arxiv-sanity.com/
Paper title: Automatic Prediction of Building Age from Photographs
Abstract: We present a first method for the automated age estimation of buildings from unconstrained photographs. To this end, we propose a two-stage approach that firstly learns characteristic visual patterns for different building epochs at patch-level and then globally aggregates patch-level age estimates over the building. We compile evaluation data sets from different sources and perform an detailed evaluation of our approach, its sensitivity to parameters, and the capabilities of the employed deep networks to learn characteristic visual age-related patterns. Results show that our approach is able to estimate building age at a surprisingly high level that even outperforms human evaluators and thereby sets a new performance baseline. This work represents a first step towards the automated assessment of building parameters for automated price prediction.

Reinforcement learning is concerned with how agents ought to take actions in an unknown environment so as to maximize so...
22/04/2018

Reinforcement learning is concerned with how agents ought to take actions in an unknown environment so as to maximize some cumulative reward over time. Namely, popular reinforcement learning algorithms such as Q-learning and Rmax are developed and deployed by agents as to learn an optimal mapping from states in the environment to desired actions.

The use of reinforcement learning for the prediction of human decision-making takes one of two forms.
- (1) Approximating and predicting how human decisions evolve in reinforcement learning environments (e.g., repeated play of an unknown game). Erev and Roth found that reinforcement learning models outperform baseline models in modeling and predicting how human decisions change and adapt in a broad range of repeated economical environments, - (2) By using a variant of reinforcement learning called inverse reinforcement learning, an agent can model human decision-making from a set of human-generated demonstrations (i.e., training examples).

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Tiedä ensimmäisenä ja anna meille oikeus lähettää sinulle sähköpostitse uutisia ja promootioita NFC :ltä. Sähköpostiosoitettasi ei käytetä muihin tarkoituksiin, ja voit perua milloin tahansa.

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