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人工智能资料库:第66辑(20170830)

AllenOR灵感
 AllenOR灵感
发布于 2017/09/10 01:26
字数 477
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1.【博客】Neural Nets for Generating Music

简介:

Algorithmic music composition has developed a lot in the last few years, but the idea has a long history. In some sense, the first automatic music came from nature: Chinese windchimes, ancient Greek wind-powered Aeolian harps, or the Japanese water instrument suikinkutsu. But in the 1700s music became “algorithmic”: Musikalisches Würfelspiel, a game that generates short piano compositions from fragments, with choices made by dice.

原文链接:https://medium.com/artists-and-machine-intelligence/neural-nets-for-generating-music-f46dffac21c0


  1. 【数据集】Fashion-MNIST

简介:

A dataset of Zalando's article images consisting of a training set of 60,000 examples and a test set of 10,000 examples. Each example is a 28x28 grayscale image, associated with a label from 10 classes. Fashion-MNIST
is intended to serve as a direct drop-in replacement of the original MNIST dataset for benchmarking machine learning algorithms.

Here is an example how the data looks like (each class takes three-rows):


原文链接:https://github.com/zalandoresearch/fashion-mnist/blob/master/README.md


3.【论文】k-Nearest Neighbor Augmented Neural Networks for Text Classification

简介:

In recent years, many deep-learning based models are proposed for text classification. This kind of models well fits the training set from the statistical point of view. However, it lacks the capacity of utilizing instance-level information from individual instances in the training set. In this work, we propose to enhance neural network models by allowing them to leverage information from k-nearest neighbor (kNN) of the input text. Our model employs a neural network that encodes texts into text embeddings. Moreover, we also utilize k-nearest neighbor of the input text as an external memory, and utilize it to capture instance-level information from the training set. The final prediction is made based on features from both the neural network encoder and the kNN memory. Experimental results on several standard benchmark datasets show that our model outperforms the baseline model on all the datasets, and it even beats a very deep neural network model (with 29 layers) in several datasets. Our model also shows superior performance when training instances are scarce, and when the training set is severely unbalanced. Our model also leverages techniques such as semi-supervised training and transfer learning quite well.

原文链接:https://arxiv.org/pdf/1708.07863.pdf


4.【博客】Hacks for training RL systems from John Schulman's lecture at Deep RL Bootcamp (Aug 2017)

简介:

From a talk given by John Schulman titled "The Nuts and Bolts of Deep RL Research" (Aug 2017)These are tricks written down while attending summer Deep RL Bootcamp at UC Berkeley.

原文链接:https://github.com/williamFalcon/DeepRLHacks


5.【代码】Collection of generative models in Tensorflow

简介:

Tensorflow implementation of various GANs and VAEs.


原文链接:https://github.com/hwalsuklee/tensorflow-generative-model-collections


本文转载自:http://www.jianshu.com/p/a0403039305b

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