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Sun 20 November 2016. Non-Zero Initial States for Recurrent Neural Networks. Tue 15 November 2016. Recurrent Neural Networks in Tensorflow III - Variable Length Sequences. This is the third in a series of posts about recurrent neural networks in Tensorflow. In this post, we'll use Tensorflow to construct an RNN that operates on input sequences of variable lengths. Sat 24 September 2016. Binary Stochastic Neurons in Tensorflow. Tue 16 August 2016. Preliminary Note on the Complexity of a Neural Network.

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Sun 20 November 2016. Non-Zero Initial States for Recurrent Neural Networks. Tue 15 November 2016. Recurrent Neural Networks in Tensorflow III - Variable Length Sequences. This is the third in a series of posts about recurrent neural networks in Tensorflow. In this post, we'll use Tensorflow to construct an RNN that operates on input sequences of variable lengths. Sat 24 September 2016. Binary Stochastic Neurons in Tensorflow. Tue 16 August 2016. Preliminary Note on the Complexity of a Neural Network.

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Sun 20 November 2016. Non-Zero Initial States for Recurrent Neural Networks. Tue 15 November 2016. Recurrent Neural Networks in Tensorflow III - Variable Length Sequences. This is the third in a series of posts about recurrent neural networks in Tensorflow. In this post, we'll use Tensorflow to construct an RNN that operates on input sequences of variable lengths. Sat 24 September 2016. Binary Stochastic Neurons in Tensorflow. Tue 16 August 2016. Preliminary Note on the Complexity of a Neural Network.

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Styles of Truncated Backpropagation - R2RT

http://r2rt.com/styles-of-truncated-backpropagation.html

Styles of Truncated Backpropagation. Tue 19 July 2016. In my post on Recurrent Neural Networks in Tensorflow. Because a well-implemented evenly-distributed truncated backpropagation algorithm would run about as fast as full backpropagation over the sequence, and full backpropagation performs slightly better, it is most likely not worth implementing such an algorithm. Differences in styles of truncated backpropagation. To deal with this, we might implement truncated backpropagation. A good description...

2

Inverting a Neural Net - R2RT

http://r2rt.com/inverting-a-neural-net.html

Inverting a Neural Net. Tue 05 April 2016. In this experiment, I invert a simple two-layer MNIST model to visualize what the final hidden layer representations look like when projected back into the original sample space. At the time of writing this post, I did not know what an autoencoder was.]. This is a fully-connected model with two-hidden layers of 100 hidden neurons. The model also contains inverse weight matrices (. That are trained after the fact by minimizing the l1 difference (. P norm x =.

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R2RT

http://r2rt.com/index2.html

Mon 11 April 2016. In this post, I offer the results of an experiment providing support for "first convergence bias", which includes the proposition that training a randomly initialized network via backpropagation may never converge to a global minimum, regardless of the intialization and number of trials. Tue 05 April 2016. Inverting a Neural Net. Wed 30 March 2016. Representational Power of Deeper Layers. Tue 29 March 2016. Implementing Batch Normalization in Tensorflow. Batch normalization is deep lea...

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Non-Zero Initial States for Recurrent Neural Networks - R2RT

http://r2rt.com/non-zero-initial-states-for-recurrent-neural-networks.html

Non-Zero Initial States for Recurrent Neural Networks. Sun 20 November 2016. Training the initial state. I haven’t seen anyone evaluate this technique (edit 11/22/16: though it appears to be common knowledge), and so I don’t have a good citation for empirical results. Instead, please see the experimental results in this post. Using a noisy initial state. One simple solution is to make the initial state noisy. This is the approach suggested by Zimmerman et al. (2012). Np, tensorflow as. Batch size: int, f...

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Written Memories: Understanding, Deriving and Extending the LSTM - R2RT

http://r2rt.com/written-memories-understanding-deriving-and-extending-the-lstm.html

Written Memories: Understanding, Deriving and Extending the LSTM. Tue 26 July 2016. In this post, we do a few things:. We’ll define and describe RNNs generally, focusing on the limitations of vanilla RNNs that led to the development of the LSTM. We’ll then extend these intuitions to show how they lead directly to a few recent and exciting architectures: highway and residual networks, and Neural Turing Machines. And Recurrent Neural Networks in Tensorflow II. Contents / quick links:. The LSTM with peephol...

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Silviu Pitis

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I study machine learning and artificial intelligence and keep a blog at r2rt.com. I'm currently attending the Recurse Center. I used to keep an economics blog.

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R2RT

Sun 20 November 2016. Non-Zero Initial States for Recurrent Neural Networks. Tue 15 November 2016. Recurrent Neural Networks in Tensorflow III - Variable Length Sequences. This is the third in a series of posts about recurrent neural networks in Tensorflow. In this post, we'll use Tensorflow to construct an RNN that operates on input sequences of variable lengths. Sat 24 September 2016. Binary Stochastic Neurons in Tensorflow. Tue 16 August 2016. Preliminary Note on the Complexity of a Neural Network.

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