SOTAVerified

Computational Efficiency

Methods and optimizations to reduce the computational resources (e.g., time, memory, or power) needed for training and inference in models. This involves techniques that streamline processing, optimize algorithms, or leverage hardware to enhance performance without compromising accuracy.

Papers

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Showing 1911–1920 of 4891 papers

TitleStatusHype
Federated Fine-Tuning of LLMs on the Very Edge: The Good, the Bad, the Ugly—0
Federated Instruction Tuning of LLMs with Domain Coverage Augmentation—0
Federated K-Means Clustering via Dual Decomposition-based Distributed Optimization—0
Federated Learning for Coronary Artery Plaque Detection in Atherosclerosis Using IVUS Imaging: A Multi-Hospital Collaboration—0
CODEI: Resource-Efficient Task-Driven Co-Design of Perception and Decision Making for Mobile Robots Applied to Autonomous Vehicles—0
Federated Learning for Medical Image Classification: A Comprehensive Benchmark—0
Federated Learning for Short-term Residential Load Forecasting—0
Federated Learning for Sparse Principal Component Analysis—0
A Non-commutative Bilinear Model for Answering Path Queries in Knowledge Graphs—0
FacEnhance: Facial Expression Enhancing with Recurrent DDPMs—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified