31,400 research outputs found
Stacking-based Deep Neural Network: Deep Analytic Network on Convolutional Spectral Histogram Features
Stacking-based deep neural network (S-DNN), in general, denotes a deep neural
network (DNN) resemblance in terms of its very deep, feedforward network
architecture. The typical S-DNN aggregates a variable number of individually
learnable modules in series to assemble a DNN-alike alternative to the targeted
object recognition tasks. This work likewise devises an S-DNN instantiation,
dubbed deep analytic network (DAN), on top of the spectral histogram (SH)
features. The DAN learning principle relies on ridge regression, and some key
DNN constituents, specifically, rectified linear unit, fine-tuning, and
normalization. The DAN aptitude is scrutinized on three repositories of varying
domains, including FERET (faces), MNIST (handwritten digits), and CIFAR10
(natural objects). The empirical results unveil that DAN escalates the SH
baseline performance over a sufficiently deep layer.Comment: 5 page
News Session-Based Recommendations using Deep Neural Networks
News recommender systems are aimed to personalize users experiences and help
them to discover relevant articles from a large and dynamic search space.
Therefore, news domain is a challenging scenario for recommendations, due to
its sparse user profiling, fast growing number of items, accelerated item's
value decay, and users preferences dynamic shift. Some promising results have
been recently achieved by the usage of Deep Learning techniques on Recommender
Systems, specially for item's feature extraction and for session-based
recommendations with Recurrent Neural Networks. In this paper, it is proposed
an instantiation of the CHAMELEON -- a Deep Learning Meta-Architecture for News
Recommender Systems. This architecture is composed of two modules, the first
responsible to learn news articles representations, based on their text and
metadata, and the second module aimed to provide session-based recommendations
using Recurrent Neural Networks. The recommendation task addressed in this work
is next-item prediction for users sessions: "what is the next most likely
article a user might read in a session?" Users sessions context is leveraged by
the architecture to provide additional information in such extreme cold-start
scenario of news recommendation. Users' behavior and item features are both
merged in an hybrid recommendation approach. A temporal offline evaluation
method is also proposed as a complementary contribution, for a more realistic
evaluation of such task, considering dynamic factors that affect global
readership interests like popularity, recency, and seasonality. Experiments
with an extensive number of session-based recommendation methods were performed
and the proposed instantiation of CHAMELEON meta-architecture obtained a
significant relative improvement in top-n accuracy and ranking metrics (10% on
Hit Rate and 13% on MRR) over the best benchmark methods.Comment: Accepted for the Third Workshop on Deep Learning for Recommender
Systems - DLRS 2018, October 02-07, 2018, Vancouver, Canada.
https://recsys.acm.org/recsys18/dlrs
TextCaps : Handwritten Character Recognition with Very Small Datasets
Many localized languages struggle to reap the benefits of recent advancements
in character recognition systems due to the lack of substantial amount of
labeled training data. This is due to the difficulty in generating large
amounts of labeled data for such languages and inability of deep learning
techniques to properly learn from small number of training samples. We solve
this problem by introducing a technique of generating new training samples from
the existing samples, with realistic augmentations which reflect actual
variations that are present in human hand writing, by adding random controlled
noise to their corresponding instantiation parameters. Our results with a mere
200 training samples per class surpass existing character recognition results
in the EMNIST-letter dataset while achieving the existing results in the three
datasets: EMNIST-balanced, EMNIST-digits, and MNIST. We also develop a strategy
to effectively use a combination of loss functions to improve reconstructions.
Our system is useful in character recognition for localized languages that lack
much labeled training data and even in other related more general contexts such
as object recognition
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