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 2751–2760 of 4891 papers

TitleStatusHype
Majority Kernels: An Approach to Leverage Big Model Dynamics for Efficient Small Model Training—0
Simulating biochemical reactions: The Linear Noise Approximation can capture non-linear dynamics—0
Simultaneous Optimal System and Controller Design for Multibody Systems with Joint Friction using Direct Sensitivities—0
Simultaneous Rectification and Alignment via Robust Recovery of Low-rank Tensors—0
Simultaneous Sampling and Multi-Structure Fitting with Adaptive Reversible Jump MCMC—0
Simultaneous Segmentation of Ventricles and Normal/Abnormal White Matter Hyperintensities in Clinical MRI using Deep Learning—0
SimVPv2: Towards Simple yet Powerful Spatiotemporal Predictive Learning—0
Since Faithfulness Fails: The Performance Limits of Neural Causal Discovery—0
Single Cell Training on Architecture Search for Image Denoising—0
Single Image Dehazing Using Scene Depth Ordering—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified