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 3221–3230 of 4891 papers

TitleStatusHype
PhAST: Physics-Aware, Scalable, and Task-specific GNNs for Accelerated Catalyst DesignCode0
SimVPv2: Towards Simple yet Powerful Spatiotemporal Predictive Learning—0
MSS-DepthNet: Depth Prediction with Multi-Step Spiking Neural Network—0
Beyond Attentive Tokens: Incorporating Token Importance and Diversity for Efficient Vision TransformersCode0
Enhancing Mobile Robot Navigation Safety and Efficiency through NMPC with Relaxed CBF in Dynamic Environments—0
Spatio-temporal point processes with deep non-stationary kernels—0
Real-time Local Feature with Global Visual Information Enhancement—0
A Transformer Framework for Data Fusion and Multi-Task Learning in Smart CitiesCode0
Brain Inspired Cortical Coding Method for Fast Clustering and Codebook Generation—0
Statistical Inference for Coadded Astronomical ImagesCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified