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Found 16 papers, 3 papers with code
X Modality Assisting RGBT Object Tracking
Learning robust multi-modal feature representations is critical for boosting tracking performance.
MG-Skip: Random Multi-Gossip Skipping Method for Nonsmooth Distributed Optimization
Distributed optimization methods with probabilistic local updates have recently gained attention for their provable ability to communication acceleration.
Revisiting Decentralized ProxSkip: Achieving Linear Speedup
We demonstrate that the leading communication complexity of ProxSkip is $\mathcal{O}\left(\frac{p\sigma^2}{n\epsilon^2}\right)$ for non-convex and convex settings, and $\mathcal{O}\left(\frac{p\sigma^2}{n\epsilon}\right)$ for the strongly convex setting, where $n$ represents the number of nodes, $p$ denotes the probability of communication, $\sigma^2$ signifies the level of stochastic noise, and $\epsilon$ denotes the desired accuracy level.
A data-driven rutting depth short-time prediction model with metaheuristic optimization for asphalt pavements based on RIOHTrack
Rutting of asphalt pavements is a crucial design criterion in various pavement design guides.
Decentralized Inexact Proximal Gradient Method With Network-Independent Stepsizes for Convex Composite Optimization
The proposed algorithm uses uncoordinated network-independent constant stepsizes and only needs to approximately solve a sequence of proximal mappings, which is advantageous for solving decentralized composite optimization problems where the proximal mappings of the nonsmooth loss functions may not have analytical solutions.
BALPA: A Balanced Primal-Dual Algorithm for Nonsmooth Optimization with Application to Distributed Optimization
In this paper, we propose a novel primal-dual proximal splitting algorithm (PD-PSA), named BALPA, for the composite optimization problem with equality constraints, where the loss function consists of a smooth term and a nonsmooth term composed with a linear mapping.
LP-BFGS attack: An adversarial attack based on the Hessian with limited pixels
We study the attack performance and computation cost of the attack method based on the Hessian with a limited number of perturbation pixels.
DISA: A Dual Inexact Splitting Algorithm for Distributed Convex Composite Optimization
In this paper, we propose a novel Dual Inexact Splitting Algorithm (DISA) for distributed convex composite optimization problems, where the local loss function consists of a smooth term and a possibly nonsmooth term composed with a linear mapping.
Exploring Adversarial Examples and Adversarial Robustness of Convolutional Neural Networks by Mutual Information
Adversarial training is a simple and effective defense method to improve the robustness of CNNs to adversarial examples.
Distributed Pinning Set Stabilization of Large-Scale Boolean Networks
In this article, we design the distributed pinning controllers to globally stabilize a Boolean network (BN), specially a sparsely connected large-scale one, towards a preassigned subset of state space through the node-to-node message exchange.
Polynomial-Time Algorithms for Structurally Observable Graphs by Controlling Minimal Vertices
Further, two minimum realization strategies are considered to induce an SOG from an arbitrarily given digraph by marking and controlling the minimal vertices, respectively.
A SOM-based Gradient-Free Deep Learning Method with Convergence Analysis
As gradient descent method in deep learning causes a series of questions, this paper proposes a novel gradient-free deep learning structure.
STCNet: Spatio-Temporal Cross Network for Industrial Smoke Detection
To overcome these problems, a novel Spatio-Temporal Cross Network (STCNet) is proposed to recognize industrial smoke emissions.
A new approach to descriptors generation for image retrieval by analyzing activations of deep neural network layers
The idea of neural codes, based on fully connected layers activations, is extended by incorporating the information contained in convolutional layers.
Distributed Pinning Control Design for Probabilistic Boolean Networks
Owing to the stochasticity, the uniform state feedback controllers, which is independent of switching signal, might be out of work.
Sensors Design for Large-Scale Boolean Networks via Pinning Observability
In this paper, a set of sensors is constructed via the pinning observability approach with the help of observability criteria given in [1] and [2], in order to make the given Boolean network (BN) be observable.
