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Found 12 papers, 6 papers with code
VDTuner: Automated Performance Tuning for Vector Data Management Systems
However, due to the inherent characteristics of VDMS, automatic performance tuning for VDMS faces several critical challenges, which cannot be well addressed by the existing auto-tuning methods.
pFedMoE: Data-Level Personalization with Mixture of Experts for Model-Heterogeneous Personalized Federated Learning
It assigns a shared homogeneous small feature extractor and a local gating network for each client's local heterogeneous large model.
DDistill-SR: Reparameterized Dynamic Distillation Network for Lightweight Image Super-Resolution
Specifically, we propose a plug-in reparameterized dynamic unit (RDU) to promote the performance and inference cost trade-off.
FedSSA: Semantic Similarity-based Aggregation for Efficient Model-Heterogeneous Personalized Federated Learning
Existing MHPFL approaches often rely on a public dataset with the same nature as the learning task, or incur high computation and communication costs.
pFedES: Model Heterogeneous Personalized Federated Learning with Feature Extractor Sharing
To allow each data owner (a. k. a., FL clients) to train a heterogeneous and personalized local model based on its local data distribution, system resources and requirements on model structure, the field of model-heterogeneous personalized federated learning (MHPFL) has emerged.
pFedLoRA: Model-Heterogeneous Personalized Federated Learning with LoRA Tuning
Federated learning (FL) is an emerging machine learning paradigm in which a central server coordinates multiple participants (clients) collaboratively to train on decentralized data.
FedGH: Heterogeneous Federated Learning with Generalized Global Header
It is a communication and computation-efficient model-heterogeneous FL framework which trains a shared generalized global prediction header with representations extracted by heterogeneous extractors for clients' models at the FL server.
Out-of-Distribution Detection based on In-Distribution Data Patterns Memorization with Modern Hopfield Energy
In more detail, penultimate layer outputs on the training set are considered as the representations of in-distribution (ID) data.
Ranked #11 on
Out-of-Distribution Detection
on ImageNet-1k vs Places
Multi-scale Attention Network for Single Image Super-Resolution
ConvNets can compete with transformers in high-level tasks by exploiting larger receptive fields.
Slim-DP: A Light Communication Data Parallelism for DNN
However, with the increasing size of DNN models and the large number of workers in practice, this typical data parallelism cannot achieve satisfactory training acceleration, since it usually suffers from the heavy communication cost due to transferring huge amount of information between workers and the parameter server.
Ensemble-Compression: A New Method for Parallel Training of Deep Neural Networks
In this framework, we propose to aggregate the local models by ensemble, i. e., averaging the outputs of local models instead of the parameters.
On the Depth of Deep Neural Networks: A Theoretical View
First, we derive an upper bound for RA of DNN, and show that it increases with increasing depth.
