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 901–910 of 4891 papers

TitleStatusHype
Inference Scaling vs Reasoning: An Empirical Analysis of Compute-Optimal LLM Problem-SolvingCode0
Inferring directed spectral information flow between mixed-frequency time seriesCode0
A differentiable programming framework for spin modelsCode0
Model Input-Output Configuration Search with Embedded Feature Selection for Sensor Time-series and Image ClassificationCode0
Innovative LSGTime Model for Crime Spatiotemporal Prediction Based on MindSpore FrameworkCode0
ColorMamba: Towards High-quality NIR-to-RGB Spectral Translation with MambaCode0
Hyperbolic Procrustes Analysis Using Riemannian GeometryCode0
clcNet: Improving the Efficiency of Convolutional Neural Network using Channel Local ConvolutionsCode0
Combinatorial Logistic BanditsCode0
HyP-DESPOT: A Hybrid Parallel Algorithm for Online Planning under UncertaintyCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified