Search Results for author:
Found 6 papers, 2 papers with code
SAM-I-Am: Semantic Boosting for Zero-shot Atomic-Scale Electron Micrograph Segmentation
To enable rapid adaptation of the best segmentation technology, we propose the concept of semantic boosting: given a zero-shot foundation model, guide its segmentation and adjust results to match domain expectations.
The Landscape and Challenges of HPC Research and LLMs
Recently, language models (LMs), especially large language models (LLMs), have revolutionized the field of deep learning.
LEFL: Low Entropy Client Sampling in Federated Learning
This enables the server to perform stratified client sampling across clusters in every round.
Addressing Data Heterogeneity in Decentralized Learning via Topological Pre-processing
In this paper, we demonstrate the advantages of constructing a proxy-based locally heterogeneous DL topology to enhance convergence and maintain data privacy.
Resource-Aware Heterogeneous Federated Learning using Neural Architecture Search
RaFL allocates resource-aware models to edge devices using Neural Architecture Search~(NAS) and allows heterogeneous model architecture deployment by knowledge extraction and fusion.
SPATL: Salient Parameter Aggregation and Transfer Learning for Heterogeneous Clients in Federated Learning
Federated learning~(FL) facilitates the training and deploying AI models on edge devices.
