Research

Contextual Recurrent Neural Networks

There is an implicit assumption that by unfolding recurrent neural networks (RNN) in finite time, the misspecification of choosing a zero value for the initial hidden state is mitigated by later time steps. This assumption has been shown to work in practice and alternative initialization may be suggested but often overlooked. In this paper, we propose a method of parameterizing the initial hidden state of an RNN. The resulting architecture, referred to as a Contextual RNN, can be trained end-to-end. The performance on an associative retrieval task is found to improve by conditioning the RNN initial hidden state on contextual information from the input sequence. Furthermore, we propose a novel method of conditionally generating sequences using the hidden state parameterization of Contextual RNN.

TensorFlow from C++

A working example of loading a TensorFlow graph in C++


An LSTM Odyssey

Discusses the paper LSTM: A Search Space Odyssey. We implemented each of the variants in TensorFlow…


TensorFlow on Kubernetes

While GPUs are a staple of deep learning, deploying on GPUs makes everything more complicated,…


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