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Found 13 papers, 7 papers with code
Active Noise Control Portable Device Design
Then the noise will be sent to an electronic control system to process the noise, which will generate a reverse phase frequency signal to counteract the disturbance.
Aggregating Intrinsic Information to Enhance BCI Performance through Federated Learning
Though numerous research groups and institutes collect a multitude of EEG datasets for the same BCI task, sharing EEG data from multiple sites is still challenging due to the heterogeneity of devices.
Waiting, Banning, and Embracing: An Empirical Analysis of Adapting Policies for Generative AI in Higher Education
This study aims to understand how universities establish policies regarding the use of AI tools and explore the factors that influence their decisions.
Efficient Training of Large-scale Industrial Fault Diagnostic Models through Federated Opportunistic Block Dropout
Artificial intelligence (AI)-empowered industrial fault diagnostics is important in ensuring the safe operation of industrial applications.
FedOBD: Opportunistic Block Dropout for Efficiently Training Large-scale Neural Networks through Federated Learning
To the best of our knowledge, FedOBD is the first approach to perform dropout on FL models at the block level rather than at the individual parameter level.
GTG-Shapley: Efficient and Accurate Participant Contribution Evaluation in Federated Learning
In addition, we design a guided Monte Carlo sampling approach combined with within-round and between-round truncation to further reduce the number of model reconstructions and evaluations required, through extensive experiments under diverse realistic data distribution settings.
FocusNetv2: Imbalanced Large and Small Organ Segmentation with Adversarial Shape Constraint for Head and Neck CT Images
Radiotherapy is a treatment where radiation is used to eliminate cancer cells.
HYDRA: Hypergradient Data Relevance Analysis for Interpreting Deep Neural Networks
the weights of training data, HYDRA assesses the contribution of training data toward test data points throughout the training trajectory.
RPN: A Residual Pooling Network for Efficient Federated Learning
Federated learning is a distributed machine learning framework which enables different parties to collaboratively train a model while protecting data privacy and security.
FedVision: An Online Visual Object Detection Platform Powered by Federated Learning
Federated learning (FL) is a promising approach to resolve this challenge.
FocusNet: Imbalanced Large and Small Organ Segmentation with an End-to-End Deep Neural Network for Head and Neck CT Images
In this paper, we propose an end-to-end deep neural network for solving the problem of imbalanced large and small organ segmentation in head and neck (HaN) CT images.
mu-Forcing: Training Variational Recurrent Autoencoders for Text Generation
It has been previously observed that training Variational Recurrent Autoencoders (VRAE) for text generation suffers from serious uninformative latent variables problem.
Impact Excitation of a Seismic Pulse and Vibrational Normal Modes on Asteroid Bennu and Associated Slumping of Regolith
With mass-spring model elastic simulations, we model impact excitation of seismic waves with a force pulse exerted on the surface and using three different asteroid shape models.
Earth and Planetary Astrophysics
