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 2141–2150 of 4891 papers

TitleStatusHype
LiteVAE: Lightweight and Efficient Variational Autoencoders for Latent Diffusion Models—0
An Empirical Study of Training State-of-the-Art LiDAR Segmentation Models—0
Graphlets correct for the topological information missed by random walks—0
Unchosen Experts Can Contribute Too: Unleashing MoE Models' Power by Self-ContrastCode1
Graph neural networks informed locally by thermodynamicsCode0
3DSS-Mamba: 3D-Spectral-Spatial Mamba for Hyperspectral Image Classification—0
Enhancing Transformer-based models for Long Sequence Time Series Forecasting via Structured MatrixCode0
Efficient Economic Model Predictive Control of Water Treatment Process with Learning-based Koopman Operator—0
Learning Causal Dynamics Models in Object-Oriented EnvironmentsCode0
Wav-KAN: Wavelet Kolmogorov-Arnold NetworksCode2
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified