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 2891–2900 of 4891 papers

TitleStatusHype
ConvNeXt-ChARM: ConvNeXt-based Transform for Efficient Neural Image Compression—0
Operational Support Estimator NetworksCode0
Predicting small molecules solubilities on endpoint devices using deep ensemble neural networksCode1
Metropolis Sampling for Constrained Diffusion Models—0
Ferroelectric MirrorBit-Integrated Field-Programmable Memory Array for TCAM, Storage, and In-Memory Computing Applications—0
Differentiable Turbulence: Closure as a partial differential equation constrained optimization—0
Application of Spherical Convolutional Neural Networks to Image Reconstruction and Denoising in Nuclear Medicine—0
TransformerG2G: Adaptive time-stepping for learning temporal graph embeddings using transformersCode0
Deep Speech Synthesis from MRI-Based Articulatory RepresentationsCode1
Remote Sensing Image Change Detection with Graph InteractionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified