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人工智能资料库:第8辑(20170112)

AllenOR灵感
 AllenOR灵感
发布于 2017/09/10 01:25
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  1. 【论文&代码】The Predictron: End-To-End Learning and Planning

简介:

One of the key challenges of artificial intelligence is to learn models that are effective in the context of planning. In this document we introduce the predictron architecture. The predictron consists of a fully abstract model, represented by a Markov reward process, that can be rolled forward multiple “imagined” planning steps. Each forward pass of the predictron accumulates internal rewards and values over multiple planning depths. The predictron is trained end-to-end so as to make these accumulated values accurately approximate the true value function. We applied the predictron to procedurally generated random mazes and a simulator for the game of pool. The predictron yielded significantly more accurate predictions than conventional deep neural network architectures.

原文链接:https://arxiv.org/pdf/1612.08810v1.pdf

代码链接:https://github.com/zhongwen/predictron


2.【论文&代码】Sentiment Analysis with Social Attention

简介:

Variation in language is ubiquitous, particularly in newer forms of writing such as social media. Fortunately, variation is not random; it is often linked to social properties of the author. In this paper, we show how to exploit social networks to make sentiment analysis more robust to social language variation. The key idea is linguistic homophily: the tendency of socially linked individuals to use language in similar ways. We formalize this idea in a novel attention-based neural network architecture, in which attention is divided among several basis models, depending on the author’s position in the social network. This has the effect of smoothing the classification function across the social network, and makes it possible to induce personalized classifiers even for authors for whom there is no labeled data or demographic metadata. This model significantly improves the accuracies of sentiment analysis on Twitter and review data.

原文链接:https://arxiv.org/pdf/1511.06052v3.pdf

代码链接:https://github.com/yiyang-gt/social-attention


3.【课程】Social Media & Text Analytics

简介:

这是一个使用机器学习、深度学习去做社交网络文本分析的课程,里面有一篇讲解Twitter API的文章非常好。

原文链接:http://socialmedia-class.org/syllabus.html


4.【课程】Deep Learning for Self-Driving Cars

简介:

This class is an introduction to the practice of deep learning through the applied theme of building a self-driving car. It is open to beginners and is designed for those who are new to machine learning, but it can also benefit advanced researchers in the field looking for a practical overview of deep learning methods and their application.

原文链接:http://selfdrivingcars.mit.edu/


5.【教程】Visual Turing Test

简介:


这是一篇关于视觉图灵测试的教程,教程所采用的框架是 Keras/Theano。

原文链接:https://github.com/mateuszmalinowski/visual_turing_test-tutorial


本文转载自:http://www.jianshu.com/p/5c710205f3fb

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