A Peek at Trends in Machine Learning

The arxiv singularity

Deep Learning Frameworks

% of papers 	 framework 	 has been around for (months)
------------------------------------------------------------
9.1 tensorflow 16
7.1 caffe 37
4.6 theano 54
3.3 torch 37
2.5 keras 19
1.7 matconvnet 26
1.2 lasagne 23
0.5 chainer 16
0.3 mxnet 17
0.3 cntk 13
0.2 pytorch 1
0.1 deeplearning4j 14

ConvNet Models

Optimization algorithms

Researchers

Hot or Not Keywords

Top hot keywords

8.17394726486 resnet
6.76767676768 tensorflow
5.21818181818 gans
5.0098386462 residual networks
4.34787878788 adam
2.95181818182 batch normalization
2.61663993305 fcn
2.47812783318 vgg16
2.03636363636 style transfer
1.99958217686 gated
1.99057177616 deep reinforcement
1.98428686543 lstm
1.93700787402 nmt
1.90606060606 inception
1.8962962963 siamese
1.88976377953 character level
1.87533998187 region proposal
1.81670721817 distillation
1.81400378481 tree search
1.78578069795 torch
1.77685950413 policy gradient
1.77370153867 encoder decoder
1.74685427385 gru
1.72430399325 word2vec
1.71884293052 relu activation
1.71459655485 visual question
1.70471560525 image generation

Top not hot

0.0462375339982 fractal
0.112222705524 learning bayesian
0.123531424661 ibp
0.138351983723 texture analysis
0.152810895084 bayesian network
0.170535340862 differential evolution
0.227932960894 wavelet transform
0.24482875551 dirichlet process

Conclusion

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I like to train deep neural nets on large datasets.

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Andrej Karpathy

Andrej Karpathy

I like to train deep neural nets on large datasets.

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