13,731 research outputs found
Embedding Feature Selection for Large-scale Hierarchical Classification
Large-scale Hierarchical Classification (HC) involves datasets consisting of
thousands of classes and millions of training instances with high-dimensional
features posing several big data challenges. Feature selection that aims to
select the subset of discriminant features is an effective strategy to deal
with large-scale HC problem. It speeds up the training process, reduces the
prediction time and minimizes the memory requirements by compressing the total
size of learned model weight vectors. Majority of the studies have also shown
feature selection to be competent and successful in improving the
classification accuracy by removing irrelevant features. In this work, we
investigate various filter-based feature selection methods for dimensionality
reduction to solve the large-scale HC problem. Our experimental evaluation on
text and image datasets with varying distribution of features, classes and
instances shows upto 3x order of speed-up on massive datasets and upto 45% less
memory requirements for storing the weight vectors of learned model without any
significant loss (improvement for some datasets) in the classification
accuracy. Source Code: https://cs.gmu.edu/~mlbio/featureselection.Comment: IEEE International Conference on Big Data (IEEE BigData 2016
Semantic Wide and Deep Learning for Detecting Crisis-Information Categories on Social Media
When crises hit, many flog to social media to share or consume information related to the event. Social media posts during crises tend to provide valuable reports on affected people, donation offers, help requests, advice provision, etc. Automatically identifying the category of information (e.g., reports on affected individuals, donations and volunteers) contained in these posts is vital for their efficient handling and consumption by effected communities and concerned organisations. In this paper, we introduce Sem-CNN; a wide and deep Convolutional Neural Network (CNN) model designed for identifying the category of information contained in crisis-related social media content. Unlike previous models, which mainly rely on the lexical representations of words in the text, the proposed model integrates an additional layer of semantics that represents the named entities in the text, into a wide and deep CNN network. Results show that the Sem-CNN model consistently outperforms the baselines which consist of
statistical and non-semantic deep learning models
Evaluation of Deep Convolutional Nets for Document Image Classification and Retrieval
This paper presents a new state-of-the-art for document image classification
and retrieval, using features learned by deep convolutional neural networks
(CNNs). In object and scene analysis, deep neural nets are capable of learning
a hierarchical chain of abstraction from pixel inputs to concise and
descriptive representations. The current work explores this capacity in the
realm of document analysis, and confirms that this representation strategy is
superior to a variety of popular hand-crafted alternatives. Experiments also
show that (i) features extracted from CNNs are robust to compression, (ii) CNNs
trained on non-document images transfer well to document analysis tasks, and
(iii) enforcing region-specific feature-learning is unnecessary given
sufficient training data. This work also makes available a new labelled subset
of the IIT-CDIP collection, containing 400,000 document images across 16
categories, useful for training new CNNs for document analysis
A deep matrix factorization method for learning attribute representations
Semi-Non-negative Matrix Factorization is a technique that learns a
low-dimensional representation of a dataset that lends itself to a clustering
interpretation. It is possible that the mapping between this new representation
and our original data matrix contains rather complex hierarchical information
with implicit lower-level hidden attributes, that classical one level
clustering methodologies can not interpret. In this work we propose a novel
model, Deep Semi-NMF, that is able to learn such hidden representations that
allow themselves to an interpretation of clustering according to different,
unknown attributes of a given dataset. We also present a semi-supervised
version of the algorithm, named Deep WSF, that allows the use of (partial)
prior information for each of the known attributes of a dataset, that allows
the model to be used on datasets with mixed attribute knowledge. Finally, we
show that our models are able to learn low-dimensional representations that are
better suited for clustering, but also classification, outperforming
Semi-Non-negative Matrix Factorization, but also other state-of-the-art
methodologies variants.Comment: Submitted to TPAMI (16-Mar-2015
Fine-tuning Multi-hop Question Answering with Hierarchical Graph Network
In this paper, we present a two stage model for multi-hop question answering.
The first stage is a hierarchical graph network, which is used to reason over
multi-hop question and is capable to capture different levels of granularity
using the nature structure(i.e., paragraphs, questions, sentences and entities)
of documents. The reasoning process is convert to node classify task(i.e.,
paragraph nodes and sentences nodes). The second stage is a language model
fine-tuning task. In a word, stage one use graph neural network to select and
concatenate support sentences as one paragraph, and stage two find the answer
span in language model fine-tuning paradigm.Comment: the experience result is not as good as I excep
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