3,362 research outputs found
MARVELO: Wireless Virtual Network Embedding for Overlay Graphs with Loops
When deploying resource-intensive signal processing applications in wireless
sensor or mesh networks, distributing processing blocks over multiple nodes
becomes promising. Such distributed applications need to solve the placement
problem (which block to run on which node), the routing problem (which link
between blocks to map on which path between nodes), and the scheduling problem
(which transmission is active when). We investigate a variant where the
application graph may contain feedback loops and we exploit wireless networks?
inherent multicast advantage. Thus, we propose Multicast-Aware Routing for
Virtual network Embedding with Loops in Overlays (MARVELO) to find efficient
solutions for scheduling and routing under a detailed interference model. We
cast this as a mixed integer quadratically constrained optimisation problem and
provide an efficient heuristic. Simulations show that our approach handles
complex scenarios quickly.Comment: 6 page
Sequential Recommendation with Self-Attentive Multi-Adversarial Network
Recently, deep learning has made significant progress in the task of
sequential recommendation. Existing neural sequential recommenders typically
adopt a generative way trained with Maximum Likelihood Estimation (MLE). When
context information (called factor) is involved, it is difficult to analyze
when and how each individual factor would affect the final recommendation
performance. For this purpose, we take a new perspective and introduce
adversarial learning to sequential recommendation. In this paper, we present a
Multi-Factor Generative Adversarial Network (MFGAN) for explicitly modeling the
effect of context information on sequential recommendation. Specifically, our
proposed MFGAN has two kinds of modules: a Transformer-based generator taking
user behavior sequences as input to recommend the possible next items, and
multiple factor-specific discriminators to evaluate the generated sub-sequence
from the perspectives of different factors. To learn the parameters, we adopt
the classic policy gradient method, and utilize the reward signal of
discriminators for guiding the learning of the generator. Our framework is
flexible to incorporate multiple kinds of factor information, and is able to
trace how each factor contributes to the recommendation decision over time.
Extensive experiments conducted on three real-world datasets demonstrate the
superiority of our proposed model over the state-of-the-art methods, in terms
of effectiveness and interpretability
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