739 research outputs found
Wasserstein Distance Guided Representation Learning for Domain Adaptation
Domain adaptation aims at generalizing a high-performance learner on a target
domain via utilizing the knowledge distilled from a source domain which has a
different but related data distribution. One solution to domain adaptation is
to learn domain invariant feature representations while the learned
representations should also be discriminative in prediction. To learn such
representations, domain adaptation frameworks usually include a domain
invariant representation learning approach to measure and reduce the domain
discrepancy, as well as a discriminator for classification. Inspired by
Wasserstein GAN, in this paper we propose a novel approach to learn domain
invariant feature representations, namely Wasserstein Distance Guided
Representation Learning (WDGRL). WDGRL utilizes a neural network, denoted by
the domain critic, to estimate empirical Wasserstein distance between the
source and target samples and optimizes the feature extractor network to
minimize the estimated Wasserstein distance in an adversarial manner. The
theoretical advantages of Wasserstein distance for domain adaptation lie in its
gradient property and promising generalization bound. Empirical studies on
common sentiment and image classification adaptation datasets demonstrate that
our proposed WDGRL outperforms the state-of-the-art domain invariant
representation learning approaches.Comment: The Thirty-Second AAAI Conference on Artificial Intelligence (AAAI
2018
Offloading in Software Defined Network at Edge with Information Asymmetry: A Contract Theoretical Approach
The proliferation of highly capable mobile devices such as smartphones and
tablets has significantly increased the demand for wireless access. Software
defined network (SDN) at edge is viewed as one promising technology to simplify
the traffic offloading process for current wireless networks. In this paper, we
investigate the incentive problem in SDN-at-edge of how to motivate a third
party access points (APs) such as WiFi and smallcells to offload traffic for
the central base stations (BSs). The APs will only admit the traffic from the
BS under the precondition that their own traffic demand is satisfied. Under the
information asymmetry that the APs know more about own traffic demands, the BS
needs to distribute the payment in accordance with the APs' idle capacity to
maintain a compatible incentive. First, we apply a contract-theoretic approach
to model and analyze the service trading between the BS and APs. Furthermore,
other two incentive mechanisms: optimal discrimination contract and linear
pricing contract are introduced to serve as the comparisons of the anti adverse
selection contract. Finally, the simulation results show that the contract can
effectively incentivize APs' participation and offload the cellular network
traffic. Furthermore, the anti adverse selection contract achieves the optimal
outcome under the information asymmetry scenario.Comment: 10 pages, 9 figure
Sampled in Pairs and Driven by Text: A New Graph Embedding Framework
In graphs with rich texts, incorporating textual information with structural
information would benefit constructing expressive graph embeddings. Among
various graph embedding models, random walk (RW)-based is one of the most
popular and successful groups. However, it is challenged by two issues when
applied on graphs with rich texts: (i) sampling efficiency: deriving from the
training objective of RW-based models (e.g., DeepWalk and node2vec), we show
that RW-based models are likely to generate large amounts of redundant training
samples due to three main drawbacks. (ii) text utilization: these models have
difficulty in dealing with zero-shot scenarios where graph embedding models
have to infer graph structures directly from texts. To solve these problems, we
propose a novel framework, namely Text-driven Graph Embedding with Pairs
Sampling (TGE-PS). TGE-PS uses Pairs Sampling (PS) to improve the sampling
strategy of RW, being able to reduce ~99% training samples while preserving
competitive performance. TGE-PS uses Text-driven Graph Embedding (TGE), an
inductive graph embedding approach, to generate node embeddings from texts.
Since each node contains rich texts, TGE is able to generate high-quality
embeddings and provide reasonable predictions on existence of links to unseen
nodes. We evaluate TGE-PS on several real-world datasets, and experiment
results demonstrate that TGE-PS produces state-of-the-art results on both
traditional and zero-shot link prediction tasks.Comment: Accepted by WWW 2019 (The World Wide Web Conference. ACM, 2019
Product-based Neural Networks for User Response Prediction
Predicting user responses, such as clicks and conversions, is of great
importance and has found its usage in many Web applications including
recommender systems, web search and online advertising. The data in those
applications is mostly categorical and contains multiple fields; a typical
representation is to transform it into a high-dimensional sparse binary feature
representation via one-hot encoding. Facing with the extreme sparsity,
traditional models may limit their capacity of mining shallow patterns from the
data, i.e. low-order feature combinations. Deep models like deep neural
networks, on the other hand, cannot be directly applied for the
high-dimensional input because of the huge feature space. In this paper, we
propose a Product-based Neural Networks (PNN) with an embedding layer to learn
a distributed representation of the categorical data, a product layer to
capture interactive patterns between inter-field categories, and further fully
connected layers to explore high-order feature interactions. Our experimental
results on two large-scale real-world ad click datasets demonstrate that PNNs
consistently outperform the state-of-the-art models on various metrics.Comment: 6 pages, 5 figures, ICDM201
UNCERTAINTY ASSOCIATED WITH TRAVEL TIME PREDICTION: ADVANCED VOLATILITY APPROACHES AND ENSEMBLE METHODS
Travel time effectively measures freeway traffic conditions. Easy access to this information provides the potential to alleviate traffic congestion and to increase the reliability in road networks. Accurate travel time information through Advanced Traveler Information Systems (ATIS) can provide guidance for travelers' decisions on departure time, route, and mode choice, and reduce travelers' stress and anxiety. In addition, travel time information can be used to present the current or future traffic state in a network and provide assistance for transportation agencies in proactively developing Advanced Traffic Management System (ATMS) strategies. Despite its importance, it is still a challenging task to model and estimate travel time, as traffic often has irregular fluctuations. These fluctuations result from the interactions among different vehicle-driver combinations and exogenous factors such as traffic incidents, weather, demand, and roadway conditions. Travel time is especially sensitive to the exogenous factors when operating at or near the roadway's capacity, where congestion occurs. Small changes in traffic demand or the occurrence of an incident can greatly affect the travel time. As it is impossible to take into consideration every impact of these unpredictable exogenous factors in the modeling process, travel time prediction problem is often associated with uncertainty. This research uses innovative data mining approaches such as advanced statistical and machine learning algorithms to study uncertainty associated with travel time prediction. The final objective of this research is to develop more accurate and reliable travel time prediction models
Influence of frost damage on water penetration into neat and air entrained concrete
In service life, concrete can be damaged either by mechanical or environmental loads or by combined ones. These damages will strongly influence water movement in concrete which could later lead to more serious deteriorations. This paper applies neutron radiography to investigate the influence of frost damage on water penetration into concrete. In addition, the improvement of frost resistance by addition of air entrainment was investigated. The results indicate that it is possible to visualize penetration of water into the porous structure of concrete by neutron radiography. Further evaluation of the test data allows determining time-dependent moisture profiles quantitatively with high resolution. After concrete is damaged by freeze-thaw cycles water penetration into ordinary concrete is accelerated. It can be shown that frost damage is not equally distributed in specimens exposed to freeze-thaw cycles. Thermal gradients lead to more serious damage near the surface. The beneficial effect of air entrainment on frost resistance has been demonstrated. After 50 freeze-thaw cycles, air entrained concrete showed no measurable increase in water absorption. But layers near the surface of concrete absorbed slightly more water after 200 freeze-thaw cycles although the dynamic elastic modulus remained constant. Results presented in this paper help us to better understand mechanisms of frost damage of concrete
Persistence approximation property for operator algebras
In this paper, we define quantitative assembly maps for operator
algebras when . Moreover, we study the persistence
approximation property for quantitative -theory of filtered operator
algebras. Finally, in the case of crossed product operator algebras, we
find a sufficient condition for the persistence approximation property. This
allows to give some applications involving the coarse Baum-Connes
conjecture.Comment: 32 page
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