dataset = ArxivAbstracts(
categories='stat.ML cs.NE cs.LG math.OC',
keywords='neural network deep')
max_length = 50
sampling_temperature = 0.5
rnn_cell = rnn_cell.GRUCell
rnn_hidden = 200
rnn_layers = 2
learning_rate = 0.002
optimizer = tf.train.AdamOptimizer
gradient_clipping = 5
batch_size = 100
epochs = 100
epoch_size = 500Independent simple complexity in the relefinn first from the difficulties in
the context of deep neural networks (DNNs) as acoustic models for large
complexity of MBN is high. In this paper we propose and inves attorning data at
the input and optimization, in which data at training and test time come from
stochastic gradient descent is a popular technique, estimation of the
input-anceueds functions and the amount of computational resour stor tects of
layers can relatives, the main id a content r
----
We all liver achieves an autoencoder trained with a segnont from neural
networks for relative compared to other unsubly with churnly improves different
non-convex optimization requires from the input samples clarifies the network
connections that are comparaldable factors for encoder based on a context
created to address more constraints: each input a generalization for learning
widely as importance in machine learning tasks. Schive then shows that are able
to the size of convolutional networks
----
Not today of conven a single layers. Whereat the resulting model is training
DNNs. We also estimation of the Rprop that combines standard Rprop to
misclassification compared to traditional and interwing a context from the
sequence of convex data to aven with the cluster size and benchmark. GProp is
competitive with connection, langrast. The algorithm is evaluations. We propose
a novel settings, as mediated for a number of parameters dacaled devices. The
objective is very for rectifier LSTM RNNs
----
What does and study scy the local attributed in significant reduction in the
mathoonfer in speech recognition in WER retullide highly results are validated
task impact is not as clear applies distrigation we find from the art of
scalability in the Higing f(st factor baselives deep neural networks is a
training in the DUDE benchmark.
We study the effectiveness of our defense mor supervised learning in deep
neural networks. We efficiently convergence rates in DNNs. We hervise the
effectiveness of
----
What would represent faithfully the ratio data. This results in Marhoauth
reduction in practice. We show that deep networks have ofler an input dimension
and analysis of multiple recurrent neural networks. This structured sparsity
inducess of the input. RFN method domeFs such as the method for confidence of
the local space the state of the art tractife implements can be combined with
other quasi-Newton training matricose for search fully considerediets are
dropout have recent resurges in deep ne
----
In science and engineering, in the important improvements in the parent
extraction methods, information is propagated types of Roblers the use of to
first-order mank-drates and image recognition by hierarchically composing
simple cluster to for framework propose in other domains have been a large
number of features that are (i) discriminative loss functions for dropout in
the teculizer by the architecture of non-convex optimization procedure atters.
They are also effective for DNN as stochastic
----
Poor (even be achieved in almost any feed-forward pooling a subset of one or
more full models can be combined with other quasi-Newton training methods. In
this nets that bosed of 12 ctunes that are independent of the input-data; (ii)
in machine learning tasks. However, the ratiod to gatire to point the
employments in the DBN using the first for offoc-training for models in a CNMs,
have a similar strategy, yur advantages and outputs of our method can be
extreamization in a 2.2x) as a pigitige of
----
We study nonconvex encoder in the networks (RFNs) hasding configurations with
non-convex large-layers of images, each directions literatic for layers. More
recent results competitive strategy, in which data at training and more
difficult to parallelize. Recent Newutic systems, the desirmally parametrically
in the DNNs improves optimization technique, we extend their important and
subset of theidesteding and dast and scale in recent advances in sparse
recovery to complicated patterns of the $L_p$
