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 2811–2820 of 4891 papers

TitleStatusHype
Sparse Principal Component Analysis via Variable Projection—0
Sparse/Robust Estimation and Kalman Smoothing with Nonsmooth Log-Concave Densities: Modeling, Computation, and Theory—0
Sparse Spiking Neural Network: Exploiting Heterogeneity in Timescales for Pruning Recurrent SNN—0
Sparse-VQ Transformer: An FFN-Free Framework with Vector Quantization for Enhanced Time Series Forecasting—0
Spatially Adaptive Cloth Regression with Implicit Neural Representations—0
Spatially Covariant Lesion Segmentation—0
Spatio-temporal Causal Learning for Streamflow Forecasting—0
Spatio-temporal Fourier Transformer (StFT) for Long-term Dynamics Prediction—0
Spatio-temporal point processes with deep non-stationary kernels—0
SpecFormer: Guarding Vision Transformer Robustness via Maximum Singular Value Penalization—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified