31 research outputs found
End-to-End Multi-View Networks for Text Classification
We propose a multi-view network for text classification. Our method
automatically creates various views of its input text, each taking the form of
soft attention weights that distribute the classifier's focus among a set of
base features. For a bag-of-words representation, each view focuses on a
different subset of the text's words. Aggregating many such views results in a
more discriminative and robust representation. Through a novel architecture
that both stacks and concatenates views, we produce a network that emphasizes
both depth and width, allowing training to converge quickly. Using our
multi-view architecture, we establish new state-of-the-art accuracies on two
benchmark tasks.Comment: 6 page
Multi-View Active Learning in the Non-Realizable Case
The sample complexity of active learning under the realizability assumption
has been well-studied. The realizability assumption, however, rarely holds in
practice. In this paper, we theoretically characterize the sample complexity of
active learning in the non-realizable case under multi-view setting. We prove
that, with unbounded Tsybakov noise, the sample complexity of multi-view active
learning can be , contrasting to
single-view setting where the polynomial improvement is the best possible
achievement. We also prove that in general multi-view setting the sample
complexity of active learning with unbounded Tsybakov noise is
, where the order of is
independent of the parameter in Tsybakov noise, contrasting to previous
polynomial bounds where the order of is related to the parameter
in Tsybakov noise.Comment: 22 pages, 1 figur
aColor: Mechatronics, Machine Learning, and Communications in an Unmanned Surface Vehicle
The aim of this work is to offer an overview of the research questions,
solutions, and challenges faced by the project aColor ("Autonomous and
Collaborative Offshore Robotics"). This initiative incorporates three different
research areas, namely, mechatronics, machine learning, and communications. It
is implemented in an autonomous offshore multicomponent robotic system having
an Unmanned Surface Vehicle (USV) as its main subsystem. Our results across the
three areas of work are systematically outlined in this paper by demonstrating
the advantages and capabilities of the proposed system for different Guidance,
Navigation, and Control missions, as well as for the high-speed and long-range
bidirectional connectivity purposes across all autonomous subsystems.
Challenges for the future are also identified by this study, thus offering an
outline for the next steps of the aColor project.Comment: Paper was originally submitted to and presented in the 8th Transport
Research Arena TRA 2020, April 27-30, 2020, Helsinki, Finlan
Training spamassassin with active semi-supervised learning
Most spam filters include some automatic pattern classifiers based on machine learning and pattern recognition techniques. Such classifiers often require a large training set of labeled emails to attain a good discriminant capability between spam and legitimate emails. In addition, they must be frequently updated because of the changes introduced by spammers to their emails to evade spam filters. To address this issue active learning and semi-supervised learning techniques can be used. Many spam filters allow the user to give a feedback on personal emails automatically labeled during filter operation, and some filters include a self-training mechanism to exploit the large number of unlabeled emails collected during filter operation. However, users are usually willing to label only a few emails, and the benefits of selftraining techniques are limited. In this paper we propose an active semi-supervised learning method to better exploit unlabeled emails, which can be easily implemented as a plug-in in real spam filters. Our method is based on clustering unlabeled emails, querying the label of one email per cluster, and propagating such label to the most similar emails of the same cluster. The effectiveness of our method is evaluated using the well known open source SpamAssassin filter, on a large and publicly available corpus of real legitimate and spam emails. 1
Active learning with gaussian processes for object categorization
Discriminative methods for visual object category recognition are typically non-probabilistic, predicting class labels but not directly providing an estimate of uncertainty. Gaussian Processes (GPs) are powerful regression techniques with explicit uncertainty models; we show here how Gaussian Processes with covariance functions defined based on a Pyramid Match Kernel (PMK) can be used for probabilistic object category recognition. The uncertainty model provided by GPs offers confidence estimates at test points, and naturally allows for an active learning paradigm in which points are optimally selected for interactive labeling. We derive a novel active category learning method based on our probabilistic regression model, and show that a significant boost in classification performance is possible, especially when the amount of training data for a category is ultimately very small. 1
Cooperative Learning and its Application to Emotion Recognition from Speech
In this paper, we propose a novel method for highly efficient exploitation of unlabeled data-Cooperative Learning. Our approach consists of combining Active Learning and Semi-Supervised Learning techniques, with the aim of reducing the costly effects of human annotation. The core underlying idea of Cooperative Learning is to share the labeling work between human and machine efficiently in such a way that instances predicted with insufficient confidence value are subject to human labeling, and those with high confidence values are machine labeled. We conducted various test runs on two emotion recognition tasks with a variable number of initial supervised training instances and two different feature sets. The results show that Cooperative Learning consistently outperforms individual Active and Semi-Supervised Learning techniques in all test cases. In particular, we show that our method based on the combination of Active Learning and Co-Training leads to the same performance of a model trained on the whole training set, but using 75% fewer labeled instances. Therefore, our method efficiently and robustly reduces the need for human annotations