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 1161–1170 of 4891 papers

TitleStatusHype
An Accelerated Camera 3DMA Framework for Efficient Urban GNSS Multipath Estimation—0
Observability conditions for neural state-space models with eigenvalues and their roots of unity—0
Probabilistic Emulation of the Community Radiative Transfer Model Using Machine Learning—0
Research on Cloud Platform Network Traffic Monitoring and Anomaly Detection System based on Large Language Models—0
LLMs meet Federated Learning for Scalable and Secure IoT Management—0
A LoRA-Based Approach to Fine-Tuning LLMs for Educational Guidance in Resource-Constrained SettingsCode0
SUPRA: Subspace Parameterized Attention for Neural Operator on General Domains—0
ScaleGNN: Towards Scalable Graph Neural Networks via Adaptive High-order Neighboring Feature Fusion—0
Neural ATTF: A Scalable Solution to Lifelong Multi-Agent Path Planning—0
When Cloud Removal Meets Diffusion Model in Remote Sensing—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified