186,046 research outputs found
A Principled Approach for Learning Task Similarity in Multitask Learning
Multitask learning aims at solving a set of related tasks simultaneously, by
exploiting the shared knowledge for improving the performance on individual
tasks. Hence, an important aspect of multitask learning is to understand the
similarities within a set of tasks. Previous works have incorporated this
similarity information explicitly (e.g., weighted loss for each task) or
implicitly (e.g., adversarial loss for feature adaptation), for achieving good
empirical performances. However, the theoretical motivations for adding task
similarity knowledge are often missing or incomplete. In this paper, we give a
different perspective from a theoretical point of view to understand this
practice. We first provide an upper bound on the generalization error of
multitask learning, showing the benefit of explicit and implicit task
similarity knowledge. We systematically derive the bounds based on two distinct
task similarity metrics: H divergence and Wasserstein distance. From these
theoretical results, we revisit the Adversarial Multi-task Neural Network,
proposing a new training algorithm to learn the task relation coefficients and
neural network parameters iteratively. We assess our new algorithm empirically
on several benchmarks, showing not only that we find interesting and robust
task relations, but that the proposed approach outperforms the baselines,
reaffirming the benefits of theoretical insight in algorithm design
Modularity-Based Clustering for Network-Constrained Trajectories
We present a novel clustering approach for moving object trajectories that
are constrained by an underlying road network. The approach builds a similarity
graph based on these trajectories then uses modularity-optimization hiearchical
graph clustering to regroup trajectories with similar profiles. Our
experimental study shows the superiority of the proposed approach over classic
hierarchical clustering and gives a brief insight to visualization of the
clustering results.Comment: 20-th European Symposium on Artificial Neural Networks, Computational
Intelligence and Machine Learning (ESANN 2012), Bruges : Belgium (2012
Deep Learning for Link Prediction in Dynamic Networks using Weak Estimators
Link prediction is the task of evaluating the probability that an edge exists in a network, and it has useful applications in many domains. Traditional approaches rely on measuring the similarity between two nodes in a static context. Recent research has focused on extending link prediction to a dynamic setting, predicting the creation and destruction of links in networks that evolve over time. Though a difficult task, the employment of deep learning techniques have shown to make notable improvements to the accuracy of predictions. To this end, we propose the novel application of weak estimators in addition to the utilization of traditional similarity metrics to inexpensively build an effective feature vector for a deep neural network. Weak estimators have been used in a variety of machine learning algorithms to improve model accuracy, owing to their capacity to estimate changing probabilities in dynamic systems. Experiments indicate that our approach results in increased prediction accuracy on several real-world dynamic networks
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