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Found 14 papers, 1 papers with code
Improving Drumming Robot Via Attention Transformer Network
In this paper, we focus on the topic of drumming robots in entertainment.
Distributed Learning Meets 6G: A Communication and Computing Perspective
With the ever-improving computing capabilities and storage capacities of mobile devices in line with evolving telecommunication network paradigms, there has been an explosion of research interest towards exploring Distributed Learning (DL) frameworks to realize stringent key performance indicators (KPIs) that are expected in next-generation/6G cellular networks.
Graph Classification via Discriminative Edge Feature Learning
Three new graph datasets are constructed based on ModelNet40, ModelNet10 and ShapeNet Part datasets.
On the Efficiency of Deep Neural Networks
For given performance requirement, an efficient neural network should use the simplest network architecture with minimal number of parameters and connections.
Making Intelligent Reflecting Surfaces More Intelligent: A Roadmap Through Reservoir Computing
This article introduces a neural network-based signal processing framework for intelligent reflecting surface (IRS) aided wireless communications systems.
Beam-Guided TasNet: An Iterative Speech Separation Framework with Multi-Channel Output
The proposed framework facilitates iterative signal refinement with the guide of beamforming and seeks to reach the upper bound of the MVDR-based methods.
Differential Privacy Meets Federated Learning under Communication Constraints
The performance of federated learning systems is bottlenecked by communication costs and training variance.
Deep Echo State Q-Network (DEQN) and Its Application in Dynamic Spectrum Sharing for 5G and Beyond
However, training of both DRL and RNNs is known to be challenging requiring a large amount of training data to achieve convergence.
Simultaneous Relevance and Diversity: A New Recommendation Inference Approach
Heterogeneous inference achieves divergent relevance, where relevance and diversity support each other as two collaborating objectives in one recommendation model, and where recommendation diversity is an inherent outcome of the relevance inference process.
Delay-aware Resource Allocation in Fog-assisted IoT Networks Through Reinforcement Learning
Mobile IoT is composed by mobile IoT devices such as vehicles, wearable devices and smartphones.
RCNet: Incorporating Structural Information into Deep RNN for MIMO-OFDM Symbol Detection with Limited Training
In this paper, we investigate learning-based MIMO-OFDM symbol detection strategies focusing on a special recurrent neural network (RNN) -- reservoir computing (RC).
Big Data Meet Cyber-Physical Systems: A Panoramic Survey
{Thus, we also} provide an overview of the different security solutions proposed for CPS big data storage, access and analytics.
Distributive Dynamic Spectrum Access through Deep Reinforcement Learning: A Reservoir Computing Based Approach
To be specific, we apply the powerful machine learning tool, deep reinforcement learning (DRL), for SUs to learn "appropriate" spectrum access strategies in a distributed fashion assuming NO knowledge of the underlying system statistics.
Large Scale Artificial Neural Network Training Using Multi-GPUs
This paper describes a method for accelerating large scale Artificial Neural Networks (ANN) training using multi-GPUs by reducing the forward and backward passes to matrix multiplication.
