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Found 14 papers, 3 papers with code
On the Convergence of Federated Averaging under Partial Participation for Over-parameterized Neural Networks
Federated learning (FL) is a widely employed distributed paradigm for collaboratively training machine learning models from multiple clients without sharing local data.
A high-resolution dynamical view on momentum methods for over-parameterized neural networks
Due to the simplicity and efficiency of the first-order gradient method, it has been widely used in training neural networks.
FedSSO: A Federated Server-Side Second-Order Optimization Algorithm
In this work, we propose FedSSO, a server-side second-order optimization method for federated learning (FL).
Multiple Domain Cyberspace Attack and Defense Game Based on Reward Randomization Reinforcement Learning
In order to improve the defense ability of defender, a game model based on reward randomization reinforcement learning is proposed.
KGRGRL: A User's Permission Reasoning Method Based on Knowledge Graph Reward Guidance Reinforcement Learning
Following that, we proposed a user's permissions reasoning method based on reinforcement learning.
A Convergence Analysis of Nesterov's Accelerated Gradient Method in Training Deep Linear Neural Networks
To the best of our knowledge, this is the first theoretical guarantee for the convergence of NAG to the global minimum in training deep neural networks.
Adversarial Attack via Dual-Stage Network Erosion
Deep neural networks are vulnerable to adversarial examples, which can fool deep models by adding subtle perturbations.
Improving the Transferability of Adversarial Examples with Resized-Diverse-Inputs, Diversity-Ensemble and Region Fitting
RDIM and region fitting do not require extra running time and these three steps can be well integrated into other attacks.
Learning Coated Adversarial Camouflages for Object Detectors
An adversary can fool deep neural network object detectors by generating adversarial noises.
Provable Convergence of Nesterov's Accelerated Gradient Method for Over-Parameterized Neural Networks
Despite the fact that the objective function is non-convex and non-smooth, we show that NAG converges to a global minimum at a non-asymptotic linear rate $(1-\Theta(1/\sqrt{\kappa}))^t$, where $\kappa > 1$ is the condition number of a gram matrix and $t$ is the number of the iterations.
Gradient Descent Averaging and Primal-dual Averaging for Strongly Convex Optimization
In order to remove this factor, we first develop gradient descent averaging (GDA), which is a general projection-based dual averaging algorithm in the strongly convex setting.
Weakness Analysis of Cyberspace Configuration Based on Reinforcement Learning
To achieve these results, we pose finding attack paths as a Reinforcement Learning (RL) problem and train an agent to find multiple domain attack paths.
Making Adversarial Examples More Transferable and Indistinguishable
Fast gradient sign attack series are popular methods that are used to generate adversarial examples.
Multi-task Feature Selection based Anomaly Detection
In this paper, we apply the multi-task feature selection in network anomaly detection area which provides a powerful method to gather information from multiple traffic and detect anomalies on it simultaneously.
