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 4341–4350 of 4891 papers

TitleStatusHype
Parallel Spiking Unit for Efficient Training of Spiking Neural Networks—0
Efficient Transformations in Deep Learning Convolutional Neural Networks—0
Efficient Transformed Gaussian Process State-Space Models for Non-Stationary High-Dimensional Dynamical Systems—0
Efficient Uncertainty Propagation with Guarantees in Wasserstein Distance—0
Efficient universal shuffle attack for visual object tracking—0
Efficient Video Segmentation Using Parametric Graph Partitioning—0
Efficient Vision-Language Models by Summarizing Visual Tokens into Compact Registers—0
EffLoc: Lightweight Vision Transformer for Efficient 6-DOF Camera Relocalization—0
Effortless Cross-Platform Video Codec: A Codebook-Based Method—0
EFiGP: Eigen-Fourier Physics-Informed Gaussian Process for Inference of Dynamic Systems—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified