Search Results for author:
Found 10 papers, 3 papers with code
Revisiting Modularity Maximization for Graph Clustering: A Contrastive Learning Perspective
Another promising line of research involves the adoption of modularity maximization, a popular and effective measure for community detection, as the guiding principle for clustering tasks.
E-ANT: A Large-Scale Dataset for Efficient Automatic GUI NavigaTion
Online GUI navigation on mobile devices has driven a lot of attention recent years since it contributes to many real-world applications.
Clean-image Backdoor Attacks
To gather a significant quantity of annotated training data for high-performance image classification models, numerous companies opt to enlist third-party providers to label their unlabeled data.
Joint Local Relational Augmentation and Global Nash Equilibrium for Federated Learning with Non-IID Data
In server, GNE reaches an agreement among inconsistent and discrepant model deviations from clients to server, which encourages the global model to update in the direction of global optimum without breaking down the clients optimization toward their local optimums.
Differentially Private Learning with Per-Sample Adaptive Clipping
As one way to implement privacy-preserving AI, differentially private learning is a framework that enables AI models to use differential privacy (DP).
Deep Unified Representation for Heterogeneous Recommendation
Recommendation system has been a widely studied task both in academia and industry.
STL-SGD: Speeding Up Local SGD with Stagewise Communication Period
Distributed parallel stochastic gradient descent algorithms are workhorses for large scale machine learning tasks.
Variance Reduced Local SGD with Lower Communication Complexity
To accelerate the training of machine learning models, distributed stochastic gradient descent (SGD) and its variants have been widely adopted, which apply multiple workers in parallel to speed up training.
Faster Distributed Deep Net Training: Computation and Communication Decoupled Stochastic Gradient Descent
Nevertheless, although distributed stochastic gradient descent (SGD) algorithms can achieve a linear iteration speedup, they are limited significantly in practice by the communication cost, making it difficult to achieve a linear time speedup.
Asynchronous Stochastic Composition Optimization with Variance Reduction
Composition optimization has drawn a lot of attention in a wide variety of machine learning domains from risk management to reinforcement learning.
