238 research outputs found
Performance Evaluation of Channel Decoding With Deep Neural Networks
With the demand of high data rate and low latency in fifth generation (5G),
deep neural network decoder (NND) has become a promising candidate due to its
capability of one-shot decoding and parallel computing. In this paper, three
types of NND, i.e., multi-layer perceptron (MLP), convolution neural network
(CNN) and recurrent neural network (RNN), are proposed with the same parameter
magnitude. The performance of these deep neural networks are evaluated through
extensive simulation. Numerical results show that RNN has the best decoding
performance, yet at the price of the highest computational overhead. Moreover,
we find there exists a saturation length for each type of neural network, which
is caused by their restricted learning abilities.Comment: 6 pages, 11 figures, Latex; typos corrected; IEEE ICC 2018 to appea
AutoSTL: Automated Spatio-Temporal Multi-Task Learning
Spatio-Temporal prediction plays a critical role in smart city construction. Jointly modeling multiple spatio-temporal tasks can further promote an intelligent city life by integrating their inseparable relationship. However, existing studies fail to address this joint learning problem well, which generally solve tasks individually or a fixed task combination. The challenges lie in the tangled relation between different properties, the demand for supporting flexible combinations of tasks and the complex spatio-temporal dependency. To cope with the problems above, we propose an Automated Spatio-Temporal multi-task Learning (AutoSTL) method to handle multiple spatio-temporal tasks jointly. Firstly, we propose a scalable architecture consisting of advanced spatio-temporal operations to exploit the complicated dependency. Shared modules and feature fusion mechanism are incorporated to further capture the intrinsic relationship between tasks. Furthermore, our model automatically allocates the operations and fusion weight. Extensive experiments on benchmark datasets verified that our model achieves state-of-the-art performance. As we can know, AutoSTL is the first automated spatio-temporal multi-task learning method.<br/
A Simulation Model of New Product Diffusion Based on Small World Network
This paper studies the impact of consumer network structure on the diffusion of new products. Taking a communication product for example, we study its diffusion through simulation based on small world network. We find that the greater the proportion of initial adopters is, the faster the new products diffuses, and it’s marginal benefit is decreasing. The social network’s connection can be divided into weak links and strong links with different functions. The more the weak links are, the faster the new products diffuse. For the rise in marketing costs, enterprises need to improve the possibility of the success of new product diffusion with comprehensive consideration
AutoSTL: Automated Spatio-Temporal Multi-Task Learning
Spatio-Temporal prediction plays a critical role in smart city construction.
Jointly modeling multiple spatio-temporal tasks can further promote an
intelligent city life by integrating their inseparable relationship. However,
existing studies fail to address this joint learning problem well, which
generally solve tasks individually or a fixed task combination. The challenges
lie in the tangled relation between different properties, the demand for
supporting flexible combinations of tasks and the complex spatio-temporal
dependency. To cope with the problems above, we propose an Automated
Spatio-Temporal multi-task Learning (AutoSTL) method to handle multiple
spatio-temporal tasks jointly. Firstly, we propose a scalable architecture
consisting of advanced spatio-temporal operations to exploit the complicated
dependency. Shared modules and feature fusion mechanism are incorporated to
further capture the intrinsic relationship between tasks. Furthermore, our
model automatically allocates the operations and fusion weight. Extensive
experiments on benchmark datasets verified that our model achieves
state-of-the-art performance. As we can know, AutoSTL is the first automated
spatio-temporal multi-task learning method
When Federated Recommendation Meets Cold-Start Problem: Separating Item Attributes and User Interactions
Federated recommendation system usually trains a global model on the server
without direct access to users' private data on their own devices. However,
this separation of the recommendation model and users' private data poses a
challenge in providing quality service, particularly when it comes to new
items, namely cold-start recommendations in federated settings. This paper
introduces a novel method called Item-aligned Federated Aggregation (IFedRec)
to address this challenge. It is the first research work in federated
recommendation to specifically study the cold-start scenario. The proposed
method learns two sets of item representations by leveraging item attributes
and interaction records simultaneously. Additionally, an item representation
alignment mechanism is designed to align two item representations and learn the
meta attribute network at the server within a federated learning framework.
Experiments on four benchmark datasets demonstrate IFedRec's superior
performance for cold-start scenarios. Furthermore, we also verify IFedRec owns
good robustness when the system faces limited client participation and noise
injection, which brings promising practical application potential in
privacy-protection enhanced federated recommendation systems. The
implementation code is availableComment: Accepted as a regular paper of WWW'2
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