In this paper, we present a fully automatic brain tumor segmentation method
based on Deep Neural Networks (DNNs). The proposed networks are tailored to
glioblastomas (both low and high grade) pictured in MR images. By their very
nature, these tumors can appear anywhere in the brain and have almost any kind
of shape, size, and contrast. These reasons motivate our exploration of a
machine learning solution that exploits a flexible, high capacity DNN while
being extremely efficient. Here, we give a description of different model

In this paper, we propose a deep neural network architecture for object
recognition based on recurrent neural networks. The proposed network, called
ReNet, replaces the ubiquitous convolution+pooling layer of the deep
convolutional neural network with four recurrent neural networks that sweep
horizontally and vertically in both directions across the image. We evaluate
the proposed ReNet on three widely-used benchmark datasets; MNIST, CIFAR-10 and
SVHN. The result suggests that ReNet is a viable alternative to the deep

Distributional models that learn rich semantic word representations are a
success story of recent NLP research. However, developing models that learn
useful representations of phrases and sentences has proved far harder. We
propose using the definitions found in everyday dictionaries as a means of
bridging this gap between lexical and phrasal semantics. Neural language
embedding models can be effectively trained to map dictionary definitions
(phrases) to (lexical) representations of the words defined by those

We introduce a novel training principle for probabilistic models that is an
alternative to maximum likelihood. The proposed Generative Stochastic Networks
(GSN) framework is based on learning the transition operator of a Markov chain
whose stationary distribution estimates the data distribution. Because the
transition distribution is a conditional distribution generally involving a
small move, it has fewer dominant modes, being unimodal in the limit of small
moves. Thus, it is easier to learn, more like learning to perform supervised

Recent work on end-to-end neural network-based architectures for machine
translation has shown promising results for En-Fr and En-De translation.
Arguably, one of the major factors behind this success has been the
availability of high quality parallel corpora. In this work, we investigate how
to leverage abundant monolingual corpora for neural machine translation.
Compared to a phrase-based and hierarchical baseline, we obtain up to $1.96$
BLEU improvement on the low-resource language pair Turkish-English, and $1.59$

The task of the emotion recognition in the wild (EmotiW) Challenge is to
assign one of seven emotions to short video clips extracted from Hollywood
style movies. The videos depict acted-out emotions under realistic conditions
with a large degree of variation in attributes such as pose and illumination,
making it worthwhile to explore approaches which consider combinations of
features from multiple modalities for label assignment. In this paper we
present our approach to learning several specialist models using deep learning

Parameter-specific adaptive learning rate methods are computationally
efficient ways to reduce the ill-conditioning problems encountered when
training large deep networks. Following recent work that strongly suggests that
most of the critical points encountered when training such networks are saddle
points, we find how considering the presence of negative eigenvalues of the
Hessian could help us design better suited adaptive learning rate schemes. We
show that the popular Jacobi preconditioner has undesirable behavior in the

Neuroscientists have long criticised deep learning algorithms as incompatible
with current knowledge of neurobiology. We explore more biologically plausible
versions of deep representation learning, focusing here mostly on unsupervised
learning but developing a learning mechanism that could account for supervised,
unsupervised and reinforcement learning. The starting point is that the basic
learning rule believed to govern synaptic weight updates
(Spike-Timing-Dependent Plasticity) arises out of a simple update rule that

Neuroscientists have long criticised deep learning algorithms as incompatible
with current knowledge of neurobiology. We explore more biologically plausible
versions of deep representation learning, focusing here mostly on unsupervised
learning but developing a learning mechanism that could account for supervised,
unsupervised and reinforcement learning. The starting point is that the basic
learning rule believed to govern synaptic weight updates
(Spike-Timing-Dependent Plasticity) can be interpreted as gradient descent on

Inspired by recent work in machine translation and object detection, we
introduce an attention based model that automatically learns to describe the
content of images. We describe how we can train this model in a deterministic
manner using standard backpropagation techniques and stochastically by
maximizing a variational lower bound. We also show through visualization how
the model is able to automatically learn to fix its gaze on salient objects
while generating the corresponding words in the output sequence. We validate