549 research outputs found
Domain Adaptive Neural Networks for Object Recognition
We propose a simple neural network model to deal with the domain adaptation
problem in object recognition. Our model incorporates the Maximum Mean
Discrepancy (MMD) measure as a regularization in the supervised learning to
reduce the distribution mismatch between the source and target domains in the
latent space. From experiments, we demonstrate that the MMD regularization is
an effective tool to provide good domain adaptation models on both SURF
features and raw image pixels of a particular image data set. We also show that
our proposed model, preceded by the denoising auto-encoder pretraining,
achieves better performance than recent benchmark models on the same data sets.
This work represents the first study of MMD measure in the context of neural
networks
Medical image denoising using convolutional denoising autoencoders
Image denoising is an important pre-processing step in medical image
analysis. Different algorithms have been proposed in past three decades with
varying denoising performances. More recently, having outperformed all
conventional methods, deep learning based models have shown a great promise.
These methods are however limited for requirement of large training sample size
and high computational costs. In this paper we show that using small sample
size, denoising autoencoders constructed using convolutional layers can be used
for efficient denoising of medical images. Heterogeneous images can be combined
to boost sample size for increased denoising performance. Simplest of networks
can reconstruct images with corruption levels so high that noise and signal are
not differentiable to human eye.Comment: To appear: 6 pages, paper to be published at the Fourth Workshop on
Data Mining in Biomedical Informatics and Healthcare at ICDM, 201
Practical recommendations for gradient-based training of deep architectures
Learning algorithms related to artificial neural networks and in particular
for Deep Learning may seem to involve many bells and whistles, called
hyper-parameters. This chapter is meant as a practical guide with
recommendations for some of the most commonly used hyper-parameters, in
particular in the context of learning algorithms based on back-propagated
gradient and gradient-based optimization. It also discusses how to deal with
the fact that more interesting results can be obtained when allowing one to
adjust many hyper-parameters. Overall, it describes elements of the practice
used to successfully and efficiently train and debug large-scale and often deep
multi-layer neural networks. It closes with open questions about the training
difficulties observed with deeper architectures
Hybrid Collaborative Filtering with Autoencoders
Collaborative Filtering aims at exploiting the feedback of users to provide
personalised recommendations. Such algorithms look for latent variables in a
large sparse matrix of ratings. They can be enhanced by adding side information
to tackle the well-known cold start problem. While Neu-ral Networks have
tremendous success in image and speech recognition, they have received less
attention in Collaborative Filtering. This is all the more surprising that
Neural Networks are able to discover latent variables in large and
heterogeneous datasets. In this paper, we introduce a Collaborative Filtering
Neural network architecture aka CFN which computes a non-linear Matrix
Factorization from sparse rating inputs and side information. We show
experimentally on the MovieLens and Douban dataset that CFN outper-forms the
state of the art and benefits from side information. We provide an
implementation of the algorithm as a reusable plugin for Torch, a popular
Neural Network framework
Neural Collaborative Filtering
In recent years, deep neural networks have yielded immense success on speech
recognition, computer vision and natural language processing. However, the
exploration of deep neural networks on recommender systems has received
relatively less scrutiny. In this work, we strive to develop techniques based
on neural networks to tackle the key problem in recommendation -- collaborative
filtering -- on the basis of implicit feedback. Although some recent work has
employed deep learning for recommendation, they primarily used it to model
auxiliary information, such as textual descriptions of items and acoustic
features of musics. When it comes to model the key factor in collaborative
filtering -- the interaction between user and item features, they still
resorted to matrix factorization and applied an inner product on the latent
features of users and items. By replacing the inner product with a neural
architecture that can learn an arbitrary function from data, we present a
general framework named NCF, short for Neural network-based Collaborative
Filtering. NCF is generic and can express and generalize matrix factorization
under its framework. To supercharge NCF modelling with non-linearities, we
propose to leverage a multi-layer perceptron to learn the user-item interaction
function. Extensive experiments on two real-world datasets show significant
improvements of our proposed NCF framework over the state-of-the-art methods.
Empirical evidence shows that using deeper layers of neural networks offers
better recommendation performance.Comment: 10 pages, 7 figure
Training Neural Networks with Stochastic Hessian-Free Optimization
Hessian-free (HF) optimization has been successfully used for training deep
autoencoders and recurrent networks. HF uses the conjugate gradient algorithm
to construct update directions through curvature-vector products that can be
computed on the same order of time as gradients. In this paper we exploit this
property and study stochastic HF with gradient and curvature mini-batches
independent of the dataset size. We modify Martens' HF for these settings and
integrate dropout, a method for preventing co-adaptation of feature detectors,
to guard against overfitting. Stochastic Hessian-free optimization gives an
intermediary between SGD and HF that achieves competitive performance on both
classification and deep autoencoder experiments.Comment: 11 pages, ICLR 201
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