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Found 26 papers, 4 papers with code
Co-Optimization of Environment and Policies for Decentralized Multi-Agent Navigation
By introducing two sub-objectives of multi-agent navigation and environment optimization, we propose an $\textit{agent-environment co-optimization}$ problem and develop a $\textit{coordinated algorithm}$ that alternates between these sub-objectives to search for an optimal synthesis of agent actions and obstacle configurations in the environment; ultimately, improving the navigation performance.
On the Trade-Off between Stability and Representational Capacity in Graph Neural Networks
Analyzing the stability of graph neural networks (GNNs) under topological perturbations is key to understanding their transferability and the role of each architecture component.
An Improved Neural Network Model Based On CNN Using For Fruit Sugar Degree Detection
In this paper, we proposed a new evaluation standard derived from dataset standard deviation(STD) for evaluating detection performance, validating the viability of using an artificial neural network model to do fruit sugar degree nondestructive detection.
Limited-Memory Greedy Quasi-Newton Method with Non-asymptotic Superlinear Convergence Rate
Interestingly, our established non-asymptotic superlinear convergence rate demonstrates an explicit trade-off between the convergence speed and memory requirement, which to our knowledge, is the first of its kind.
Differentially Private Decentralized Deep Learning with Consensus Algorithms
Furthermore, we show that it is possible to learn a model achieving high accuracies, within 3% of DP-SGD on MNIST under (1, 10^-5)-differential privacy and within 6% of DP-SGD on CIFAR-100 under (10, 10^-5)-differential privacy, without ever sharing raw data with other agents.
Constrained Environment Optimization for Prioritized Multi-Agent Navigation
The goal of this paper is to consider the environment as a decision variable in a system-level optimization problem, where both agent performance and environment cost are incorporated.
Online Control Barrier Functions for Decentralized Multi-Agent Navigation
Control barrier functions (CBFs) enable guaranteed safe multi-agent navigation in the continuous domain.
Identification and Estimation of Categorical Random Coefficient Models
The utility of the proposed estimator is illustrated by estimating the distribution of returns to education in the U. S. by gender and educational levels.
AirGNNs: Graph Neural Networks over the Air
This paper proposes graph neural networks over the air (AirGNNs), a novel GNN architecture that incorporates the communication model into the architecture.
Learning Stable Graph Neural Networks via Spectral Regularization
Stability of graph neural networks (GNNs) characterizes how GNNs react to graph perturbations and provides guarantees for architecture performance in noisy scenarios.
Decentralized Channel Management in WLANs with Graph Neural Networks
Wireless local area networks (WLANs) manage multiple access points (APs) and assign scarce radio frequency resources to APs for satisfying traffic demands of associated user devices.
Environment Optimization for Multi-Agent Navigation
Traditional approaches to the design of multi-agent navigation algorithms consider the environment as a fixed constraint, despite the obvious influence of spatial constraints on agents' performance.
Few-shot Fine-grained Image Classification via Multi-Frequency Neighborhood and Double-cross Modulation
The comprehensive experiments on three fine-grained benchmark datasets for two few-shot tasks verify that FicNet has excellent performance compared to the state-of-the-art methods.
Learning Stochastic Graph Neural Networks with Constrained Variance
To overcome this issue, we propose a variance-constrained optimization problem for SGNNs, balancing the expected performance and the stochastic deviation.
Wide and Deep Graph Neural Network with Distributed Online Learning
At training time, the joint wide and deep architecture learns nonlinear representations from data.
Stability of Graph Convolutional Neural Networks to Stochastic Perturbations
In particular, it proves the expected output difference between the GCNN over random perturbed graphs and the GCNN over the nominal graph is upper bounded by a factor that is linear in the link loss probability.
Training Robust Graph Neural Networks with Topology Adaptive Edge Dropping
Graph neural networks (GNNs) are processing architectures that exploit graph structural information to model representations from network data.
Spherical Convolutional Neural Networks: Stability to Perturbations in SO(3)
Spherical convolutional neural networks (Spherical CNNs) learn nonlinear representations from 3D data by exploiting the data structure and have shown promising performance in shape analysis, object classification, and planning among others.
Resource Allocation via Model-Free Deep Learning in Free Space Optical Communications
This paper investigates the general problem of resource allocation for mitigating channel fading effects in Free Space Optical (FSO) communications.
Balancing Rates and Variance via Adaptive Batch-Size for Stochastic Optimization Problems
Stochastic gradient descent is a canonical tool for addressing stochastic optimization problems, and forms the bedrock of modern machine learning and statistics.
Resource Allocation via Graph Neural Networks in Free Space Optical Fronthaul Networks
This paper investigates the optimal resource allocation in free space optical (FSO) fronthaul networks.
Wide and Deep Graph Neural Networks with Distributed Online Learning
At testing time, the deep part (nonlinear) is left unchanged, while the wide part is retrained online, leading to a convex problem.
Stochastic Graph Neural Networks
Graph neural networks (GNNs) model nonlinear representations in graph data with applications in distributed agent coordination, control, and planning among others.
Optimal WDM Power Allocation via Deep Learning for Radio on Free Space Optics Systems
Radio on Free Space Optics (RoFSO), as a universal platform for heterogeneous wireless services, is able to transmit multiple radio frequency signals at high rates in free space optical networks.
On LASSO for Predictive Regression
This new finding motivates a novel post-selection adaptive LASSO, which we call the twin adaptive LASSO (TAlasso), to restore variable selection consistency.
Implementing Convex Optimization in R: Two Econometric Examples
Economists specify high-dimensional models to address heterogeneity in empirical studies with complex big data.
Computation Econometrics
