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 411–420 of 4891 papers

TitleStatusHype
Precision Neural Network Quantization via Learnable Adaptive Modules—0
Data-Driven Surrogate Modeling Techniques to Predict the Effective Contact Area of Rough Surface Contact Problems—0
Mixed Bernstein-Fourier Approximants for Optimal Trajectory Generation with Periodic Behavior—0
Combining GCN Structural Learning with LLM Chemical Knowledge for or Enhanced Virtual Screening—0
GotenNet: Rethinking Efficient 3D Equivariant Graph Neural NetworksCode2
An Accelerated Camera 3DMA Framework for Efficient Urban GNSS Multipath Estimation—0
Improving Significant Wave Height Prediction Using Chronos Models—0
Hyper-Transforming Latent Diffusion Models—0
ScaleGNN: Towards Scalable Graph Neural Networks via Adaptive High-order Neighboring Feature Fusion—0
Survey of Video Diffusion Models: Foundations, Implementations, and ApplicationsCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified