3,397 research outputs found
Smartphone picture organization: a hierarchical approach
We live in a society where the large majority of the population has a camera-equipped smartphone. In addition, hard drives and cloud storage are getting cheaper and cheaper, leading to a tremendous growth in stored personal photos. Unlike photo collections captured by a digital camera, which typically are pre-processed by the user who organizes them into event-related folders, smartphone pictures are automatically stored in the cloud. As a consequence, photo collections captured by a smartphone are highly unstructured and because smartphones are ubiquitous, they present a larger variability compared to pictures captured by a digital camera. To solve the need of organizing large smartphone photo collections automatically, we propose here a new methodology for hierarchical photo organization into topics and topic-related categories. Our approach successfully estimates latent topics in the pictures by applying probabilistic Latent Semantic Analysis, and automatically assigns a name to each topic by relying on a lexical database. Topic-related categories are then estimated by using a set of topic-specific Convolutional Neuronal Networks. To validate our approach, we ensemble and make public a large dataset of more than 8,000 smartphone pictures from 40 persons. Experimental results demonstrate major user satisfaction with respect to state of the art solutions in terms of organization.Peer ReviewedPreprin
Reproducible Econometric Research. A Critical Review of the State of the Art.
Recent software developments are reviewed from the vantage point of reproducible econometric research. We argue that the emergence of new tools, particularly in the open-source community, have greatly eased the burden of documenting and archiving both empirical and simulation work in econometrics. Some of these tools are highlighted in the discussion of three small replication exercises.Series: Research Report Series / Department of Statistics and Mathematic
Deep Temporal-Recurrent-Replicated-Softmax for Topical Trends over Time
Dynamic topic modeling facilitates the identification of topical trends over
time in temporal collections of unstructured documents. We introduce a novel
unsupervised neural dynamic topic model named as Recurrent Neural
Network-Replicated Softmax Model (RNNRSM), where the discovered topics at each
time influence the topic discovery in the subsequent time steps. We account for
the temporal ordering of documents by explicitly modeling a joint distribution
of latent topical dependencies over time, using distributional estimators with
temporal recurrent connections. Applying RNN-RSM to 19 years of articles on NLP
research, we demonstrate that compared to state-of-the art topic models, RNNRSM
shows better generalization, topic interpretation, evolution and trends. We
also introduce a metric (named as SPAN) to quantify the capability of dynamic
topic model to capture word evolution in topics over time.Comment: In Proceedings of the 16th Annual Conference of the North American
Chapter of the Association for Computational Linguistics: Human Language
Technologies (NAACL-HLT 2018
Domain-Adversarial Training of Neural Networks
We introduce a new representation learning approach for domain adaptation, in
which data at training and test time come from similar but different
distributions. Our approach is directly inspired by the theory on domain
adaptation suggesting that, for effective domain transfer to be achieved,
predictions must be made based on features that cannot discriminate between the
training (source) and test (target) domains. The approach implements this idea
in the context of neural network architectures that are trained on labeled data
from the source domain and unlabeled data from the target domain (no labeled
target-domain data is necessary). As the training progresses, the approach
promotes the emergence of features that are (i) discriminative for the main
learning task on the source domain and (ii) indiscriminate with respect to the
shift between the domains. We show that this adaptation behaviour can be
achieved in almost any feed-forward model by augmenting it with few standard
layers and a new gradient reversal layer. The resulting augmented architecture
can be trained using standard backpropagation and stochastic gradient descent,
and can thus be implemented with little effort using any of the deep learning
packages. We demonstrate the success of our approach for two distinct
classification problems (document sentiment analysis and image classification),
where state-of-the-art domain adaptation performance on standard benchmarks is
achieved. We also validate the approach for descriptor learning task in the
context of person re-identification application.Comment: Published in JMLR: http://jmlr.org/papers/v17/15-239.htm
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