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Found 13 papers, 5 papers with code
Secure Federated Learning Across Heterogeneous Cloud and High-Performance Computing Resources -- A Case Study on Federated Fine-tuning of LLaMA 2
Federated learning enables multiple data owners to collaboratively train robust machine learning models without transferring large or sensitive local datasets by only sharing the parameters of the locally trained models.
FedCompass: Efficient Cross-Silo Federated Learning on Heterogeneous Client Devices using a Computing Power Aware Scheduler
Nonetheless, because of the disparity of computing resources among different clients (i. e., device heterogeneity), synchronous federated learning algorithms suffer from degraded efficiency when waiting for straggler clients.
Self-Supervised Masked Digital Elevation Models Encoding for Low-Resource Downstream Tasks
The proposed architecture is the Masked Autoencoder pre-trained on ImageNet (with the limitation that there is a large domain discrepancy between ImageNet and DEM) with an UperNet Head for decoding segmentations.
APPFLx: Providing Privacy-Preserving Cross-Silo Federated Learning as a Service
Cross-silo privacy-preserving federated learning (PPFL) is a powerful tool to collaboratively train robust and generalized machine learning (ML) models without sharing sensitive (e. g., healthcare of financial) local data.
Sequence Generation via Subsequence Similarity: Theory and Application to UAV Identification
The ability to generate synthetic sequences is crucial for a wide range of applications, and recent advances in deep learning architectures and generative frameworks have greatly facilitated this process.
FAIR for AI: An interdisciplinary and international community building perspective
A foundational set of findable, accessible, interoperable, and reusable (FAIR) principles were proposed in 2016 as prerequisites for proper data management and stewardship, with the goal of enabling the reusability of scholarly data.
Weakly Supervised Two-Stage Training Scheme for Deep Video Fight Detection Model
In this paper, we propose a simple but effective method that solves the task from a new perspective: we design the fight detection model as a composition of an action-aware feature extractor and an anomaly score generator.
AGNet: Weighing Black Holes with Deep Learning
We find a 1$\sigma$ scatter of 0. 37 dex between the predicted SMBH mass and the fiducial virial mass estimate based on SDSS single-epoch spectra, which is comparable to the systematic uncertainty in the virial mass estimate.
Accelerated, Scalable and Reproducible AI-driven Gravitational Wave Detection
The development of reusable artificial intelligence (AI) models for wider use and rigorous validation by the community promises to unlock new opportunities in multi-messenger astrophysics.
Convergence of Artificial Intelligence and High Performance Computing on NSF-supported Cyberinfrastructure
Significant investments to upgrade and construct large-scale scientific facilities demand commensurate investments in R&D to design algorithms and computing approaches to enable scientific and engineering breakthroughs in the big data era.
Review and Examination of Input Feature Preparation Methods and Machine Learning Models for Turbulence Modeling
Model extrapolation to unseen flow is one of the biggest challenges facing data-driven turbulence modeling, especially for models with high dimensional inputs that involve many flow features.
Fluid Dynamics Computational Physics
Enabling real-time multi-messenger astrophysics discoveries with deep learning
Multi-messenger astrophysics is a fast-growing, interdisciplinary field that combines data, which vary in volume and speed of data processing, from many different instruments that probe the Universe using different cosmic messengers: electromagnetic waves, cosmic rays, gravitational waves and neutrinos.
Deep Learning for Multi-Messenger Astrophysics: A Gateway for Discovery in the Big Data Era
We discuss key aspects to realize this endeavor, namely (i) the design and exploitation of scalable and computationally efficient AI algorithms for Multi-Messenger Astrophysics; (ii) cyberinfrastructure requirements to numerically simulate astrophysical sources, and to process and interpret Multi-Messenger Astrophysics data; (iii) management of gravitational wave detections and triggers to enable electromagnetic and astro-particle follow-ups; (iv) a vision to harness future developments of machine and deep learning and cyberinfrastructure resources to cope with the scale of discovery in the Big Data Era; (v) and the need to build a community that brings domain experts together with data scientists on equal footing to maximize and accelerate discovery in the nascent field of Multi-Messenger Astrophysics.
