A neural network method optimizes power control in multi-user channels.
problem Maximizing sum rate in multi-user interference channels.
method PCNet, a multi-layer neural network, directly maximizes sum rate; ePCNet is an ensemble of multiple PCNets.
result ePCNet outperforms existing power control methods by 1.2%-4.6%.
This paper tackles resource allocation in multi-user communication networks using a coordinated multi-armed bandit approach.
problem Learning unknown stochastic network characteristics and sharing resources efficiently.
method Combines Multi-Armed Bandit learning with a lightweight signalling-based coordination scheme.
result Ensures convergence to a stable allocation of resources with maximal resource utilization.
This paper tackles efficient resource control in IoT edge computing using deep reinforcement learning.
problem Efficient allocation and scheduling of limited resources in IoT edge computing systems.
method Formulated as a CTMDP model, used deep reinforcement learning (RL) to approximate value functions and solve the MDP problem.
result Significant performance improvement over baseline algorithms and RL algorithms based on other architectures.
Improved multi-user VoiceFilter-Lite model for speech recognition.
problem Limited performance of multi-user VoiceFilter-Lite models.
method Dual learning rate schedule and FiLM for feature conditioning.
result Closed the performance gap between multi-user and single-user VoiceFilter-Lite models.
Algorithm optimizes spectrum access for dynamic multi-user environments.
problem Optimizing spectrum access in uncoordinated multi-user environments with potential collisions.
method Stochastic multi-user bandit framework with estimation and allocation phases.
result Order-optimal system-wide regret of O(logT) for dynamic and static cases. New policy reduces spectrum access regret in uncoordinated systems.
problem Uncoordinated spectrum access with user-dependent rewards.
method Multi-user multi-armed bandit (MAB) model with user-specific rewards.
result Achieves O(logT) regret for spectrum access. Unified framework for multi-user bandits using Laplacian kernels.
problem Multi-user contextual bandits with graph-related users and non-linear rewards.
method Joint penalty combining graph smoothness and individual roughness in a unified RKHS.
result Unified multi-user RKHS and effective dimension for regret bounds.
GUIDE-VAE generates user-guided data with improved realism and performance.
problem Generating data points for multi-user datasets while considering user information.
method Conditional generative model that integrates user embeddings and a pattern dictionary-based covariance composition.
result GUIDE-VAE outperforms conventional VAEs in multi-user settings, especially under data imbalance.
New model for personalized online advertising with multi-user interaction.
problem Realistic online advertising scenarios with multiple users interacting simultaneously.
method Introduces Multi-User Contextual Cascading Bandit (MCCB) model and proposes UCBBP and AUCBBP algorithms.
result Proves UCBBP and AUCBBP achieve optimal regret bounds for multi-user context.
Optimizes data transmission timing to minimize user information age.
problem Minimizing long-term average age of information in multi-user networks.
method Reinforcement learning applied to scheduling decisions without channel state information.
result RL approach effectively reduces AoI compared to traditional ARQ and HARQ.
New algorithms for uncoordinated spectrum access with multi-user multi-armed bandits.
problem Uncoordinated spectrum access with unknown number of users and channels.
method Developed algorithms for stochastic and adversarial settings, combining Exp3.P for dynamic scenarios.
result Sub-linear regret guarantees for both stochastic and adversarial cases, even when users outnumber channels.
Method learns gestures from touch devices, outperforming state of the art.
problem Automatic gesture recognition on touch devices with multi-user variability.
method Dynamic sampling, convolutional model, recurrent vs. convolutional features.
result Outperforms state of the art on MMG dataset.
Optimizes data power control in cell-free networks for better spectral efficiency.
problem Maximizing overall spectral efficiency in cell-free networks with multi-objective optimisation.
method Applied scalable multi-objective Bayesian optimisation to solve convergence-time limitations.
result Improved radio resource management in cell-free networks.
State-augmented algorithm optimizes wireless network resource management.
problem Optimizing resource allocation in multi-user wireless networks.
method Proposes a state-augmented algorithm using dual variables.
result Feasible and near-optimal resource decisions achieved.
Optimizes wireless network resource management with state-augmented policies.
problem Optimizing network-wide utility with user performance constraints.
method State-augmented parameterization of RRM policy, using dual variables.
result Superior trade-off between mean, minimum, and 5th percentile rates.
Radio emitter recognition in dense multi-user environments is an important tool for optimizing spectrum utilization, identifying and minimizing interference, and enforcing spectrum policy. Radio data is readily available and easy to obtain from an antenna, but labeled and curated data is often scarce making supervised …
A framework for multi-agent communication over noisy channels in reinforcement learning.
problem Effective communication between multiple agents in a noisy environment.
method A novel multi-agent partially observable Markov decision process (MA-POMDP) framework considering noisy communication channels.
result Jointly learned policies outperform separate learning of communication and decision making.
Enhances cooperative multi-task SemCom for distributed users.
problem Performance degradation in cooperative multi-tasking due to negative information transfer.
method Federated learning (FL) with semantic-aware task clustering.
result Constructive cooperation across distributed users with semantic-aware task clustering.
New algorithm optimizes beam and rate allocation in mmWave systems for multiple users.
problem Optimizing beam and rate allocation in mmWave systems for multiple users with limited feedback.
method Introducing SAT-CTS, a combinatorial semi-bandit policy with satisficing objective.
result SAT-CTS achieves finite-time regret bounds and reduces satisficing regret in mmWave systems.
Traditional activity recognition systems work on the basis of training, taking a fixed set of sensors into account. In this article, we focus on the question how pattern recognition can leverage new information sources without any, or with minimal user input. Thus, we present an approach for opportunistic activity reco…
Proposes a deep reinforcement learning framework for dynamic multichannel access.
problem Efficient use of limited spectral resources in dynamic multichannel access.
method Deep actor-critic reinforcement learning framework for both single-user and multi-user scenarios.
result Demonstrates improved performance and adaptive ability compared to existing methods.
A DRL approach optimizes computation offloading in MEC systems for mobile users.
problem Optimizing computation offloading in MEC systems with mobile users and stochastic task arrivals.
method Deep Deterministic Policy Gradient (DDPG) for decentralized dynamic computation offloading.
result The DDPG-based strategy outperforms conventional strategies in terms of computation cost and power-delay tradeoff.
DeepCMC compresses CSI for massive MIMO systems, reducing overhead and improving performance.
problem High CSI overhead in massive MIMO systems limits spectral efficiency.
method Deep learning-based fully convolutional neural network with residual layers and entropy coding.
result DeepCMC outperforms state-of-the-art schemes in CSI reconstruction quality for the same compression rate.
Adobe research tackles strategic recommendations using reinforcement learning.
problem Optimizing user interactions for long-term objectives in various use-cases.
method Reinforcement learning applied to modeling user behavior and decision-making.
result Practical solutions implemented for various use-cases, addressing fundamental challenges.
Automates feature extraction for IMU-based activity recognition.
problem Manual feature engineering is time-consuming and limits IMU-based applications.
method FRESH algorithm for automated feature extraction.
result Workflow automates feature extraction from synchronized IMU sensors.
Improved conversion prediction for B2B products using collective online activity trails.
problem Limited information from single user activity trails for B2B ad targeting.
method Introduced relevant users concept and used distributed activity representations to build seed lists.
result Improved conversion prediction AUC by 8.8% using collective activity trails.
Receiver algorithms which combine belief propagation (BP) with the mean field (MF) approximation are well-suited for inference of both continuous and discrete random variables. In wireless scenarios involving detection of multiple signals, the standard construction of the combined BP-MF framework includes the equalizat…
Survey on deep learning for social network analysis.
problem Encoding social network data into useful low-dimensional representations.
method Review of neural network models for node and subgraph embeddings in various network types.
result Advancements in deep learning for complex network analysis.
RFN improves GCNs for road networks, outperforming state-of-the-art by 21%-40%.
problem Leveraging the structure of road networks effectively in machine learning tasks.
method Introducing RFN, a novel GCN specifically designed for road networks.
result RFN outperforms state-of-the-art GCNs by 21%-40% on road network tasks.
New model reconstructs networks by identifying regular components.
problem Uncovering the complexity of network structures.
method Low-rank pursuit based self-representation network model.
result Reconstructs networks and measures their regulability.
Algorithm reconstructs conserved networks from flow data.
problem Network reconstruction from flow data.
method Polynomial time algorithm exploiting graph theoretic properties and learning techniques.
result Exact network reconstruction possible for arborescence networks.
This survey clarifies dynamic network terminology and reviews GNN models for dynamic networks.
problem Ambiguity in dynamic network terminology and lack of GNN models for dynamic networks.
method Established consistent terminology and notation for dynamic networks, reviewed GNN models.
result Comprehensive survey of dynamic graph neural network models.
DCNs mimic neuronal networks for improved neural classification.
problem Lack of topological similarity between DNNs and biological neural networks.
method Developed DCNs with topologies inspired by real-world neuronal networks.
result High classification accuracy achieved by DCNs.
Study 986 diverse networks to reveal structural diversity across domains.
problem Understanding structural diversity in networks across various domains.
method Machine learning techniques (random forest, confusion matrix) on 986 real-world networks and 575 generated networks.
result Networks in the same partition have similar underlying functions, constraints, and generative mechanisms, regardless of their origins.
Network recasting transforms network architecture for faster inference.
problem Accelerate inference process through network transformation.
method Block-wise recasting of source blocks in a teacher network to target blocks in a student network.
result Transforms network architecture while preserving accuracy and reducing inference time.
Highly accurate classification of network categories achieved.
problem Distinguishing between different types of networks (e.g., social vs. web graphs).
method Used a random forest classifier on both real-world and synthetic networks.
result Achieved a 94.2% classification accuracy.
Network Lens identifies node behaviors in heterogeneous networks with high accuracy.
problem Identifying different behaviors in various parts of large heterogeneous networks.
method Zoom into network using different-sized lenses to capture local structure, weight signatures to predict node labels.
result Achieved a peak accuracy of ~42% on two networks with ~100,000 and ~1,000,000 nodes, significantly better than random.
DANE adapts network embeddings across multiple domains.
problem Learning embeddings for multiple networks without transferability.
method Graph Convolutional Network with adversarial learning.
result DANE achieves superior performance in cross-network domain adaptation.
Capsule networks are vulnerable to adversarial attacks, similar to convolutional neural networks.
problem Vulnerability of capsule networks to adversarial attacks.
method Compared capsule networks to convolutional neural networks using various adversarial attacks.
result Capsule networks are vulnerable to adversarial attacks, similar to convolutional neural networks.
Chemical networks outperform spiking neural networks in classification tasks.
problem Learning tasks with spiking neural networks require hidden layers, which are computationally expensive.
method Used deterministic mass-action kinetics to prove chemical reaction networks without hidden layers can solve tasks previously solved by spiking neural networks.
result A chemical reaction network without hidden layers outperforms a spiking neural network with hidden layers in a handwritten digit classification task.
Deep ReLU networks can be simplified to a three-layer model.
problem Understanding the behavior of deep neural networks.
method Constructive proof and algorithm to transform deep networks into shallow ones.
result Deep ReLU networks can be represented by a simpler three-layer structure.
Tackles network structure inference from time series data using GNN.
problem Inferring network structure from incomplete or no information.
method Gumbel Graph Network (GGN) model for network reconstruction and completion.
result GGN can reconstruct up to 100% network structure and infer missing parts with up to 90% accuracy.
Paper proposes algorithms for embedding directed networks with text associated nodes.
problem Learning embeddings for directed networks with text associated nodes.
method PCTADW-1 and PCTADW-2 neural network algorithms.
result Embeddings improve node classification quality on software package dependency networks.
Network embedding helps predict speed limits on incomplete Danish road network.
problem Incomplete speed limit data on Danish roads limits machine learning applications.
method Applied node2vec network embedding to Danish road network.
result Network embedding can derive useful features for predicting speed limits.
This paper explores loss landscapes of sparse neural networks, finding unique characteristics compared to dense networks.
problem Understanding the loss landscape of sparse neural networks, especially one-hidden-layer networks.
method Analyzes sparse networks with dense and sparse final layers, focusing on linear and non-linear models.
result Sparse networks can have no spurious valleys under certain conditions, but spurious valleys and minima can exist for wide sparse networks.
New approach learns latent motifs in networks for mesoscale structure analysis.
problem Understanding large-scale behavior in complex systems through mesoscale structures.
method Network dictionary learning (NDL) combining network sampling and nonnegative matrix factorization.
result Networks can be approximated using a small set of latent motifs.
The paper surveys network methods for understanding economic and financial systems.
problem Understanding interconnectedness among economic and financial entities.
method Survey of network theory, measures, and structures for economic and financial networks.
result Network methods provide tools to quantify structural properties of economic systems.
Paper links network Lasso to network flow optimization.
problem Joint clustering and optimization of networked data.
method Exploration of duality between network Lasso and network flow optimization.
result nLasso is equivalent to a minimum-cost flow problem on the data network structure.