Search Results for author:
Found 7 papers, 4 papers with code
A Fast, Performant, Secure Distributed Training Framework For Large Language Model
In this paper, we propose a secure distributed LLM based on model slicing.
HPN: Personalized Federated Hyperparameter Optimization
The client encoding is calculated with a random projection-based procedure to protect each client's privacy.
PKD: General Distillation Framework for Object Detectors via Pearson Correlation Coefficient
First, the difference in feature magnitude between the teacher and the student could enforce overly strict constraints on the student.
Differentially Private Federated Learning with Local Regularization and Sparsification
User-level differential privacy (DP) provides certifiable privacy guarantees to the information that is specific to any user's data in federated learning.
DPNAS: Neural Architecture Search for Deep Learning with Differential Privacy
In light of this missing, we propose the very first framework that employs neural architecture search to automatic model design for private deep learning, dubbed as DPNAS.
Location-aware Upsampling for Semantic Segmentation
Many successful learning targets such as minimizing dice loss and cross-entropy loss have enabled unprecedented breakthroughs in segmentation tasks.
Ranked #35 on
Semantic Segmentation
on PASCAL Context
SpatialFlow: Bridging All Tasks for Panoptic Segmentation
Object location is fundamental to panoptic segmentation as it is related to all things and stuff in the image scene.
Ranked #17 on
Panoptic Segmentation
on COCO test-dev
