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 2881–2890 of 4891 papers

TitleStatusHype
ZeroQuant-FP: A Leap Forward in LLMs Post-Training W4A8 Quantization Using Floating-Point Formats—0
Make-A-Volume: Leveraging Latent Diffusion Models for Cross-Modality 3D Brain MRI Synthesis—0
Saliency strikes back: How filtering out high frequencies improves white-box explanations—0
Flow Matching in Latent SpaceCode2
Bivariate DeepKriging for Large-scale Spatial Interpolation of Wind Fields—0
KECOR: Kernel Coding Rate Maximization for Active 3D Object Detection—0
On the Effective Horizon of Inverse Reinforcement Learning—0
YOLIC: An Efficient Method for Object Localization and Classification on Edge DevicesCode0
TinyMetaFed: Efficient Federated Meta-Learning for TinyML—0
FDAPT: Federated Domain-adaptive Pre-training for Language ModelsCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified