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 3191–3200 of 4891 papers

TitleStatusHype
TinyMetaFed: Efficient Federated Meta-Learning for TinyML—0
On the Effective Horizon of Inverse Reinforcement Learning—0
YOLIC: An Efficient Method for Object Localization and Classification on Edge DevicesCode0
FDAPT: Federated Domain-adaptive Pre-training for Language ModelsCode0
Operational Support Estimator NetworksCode0
ConvNeXt-ChARM: ConvNeXt-based Transform for Efficient Neural Image Compression—0
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
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified