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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 511–520 of 4891 papers

TitleStatusHype
Objects do not disappear: Video object detection by single-frame object location anticipationCode1
PETformer: Long-term Time Series Forecasting via Placeholder-enhanced TransformerCode1
JEN-1: Text-Guided Universal Music Generation with Omnidirectional Diffusion ModelsCode1
Learning Fine-Grained Features for Pixel-wise Video CorrespondencesCode1
A State-Space Perspective on Modelling and Inference for Online Skill RatingCode1
Fully 11 Convolutional Network for Lightweight Image Super-ResolutionCode1
From continuous-time formulations to discretization schemes: tensor trains and robust regression for BSDEs and parabolic PDEsCode1
Variational Autoencoding of Dental Point CloudsCode1
Differentially Flat Learning-based Model Predictive Control Using a Stability, State, and Input Constraining Safety FilterCode1
Predicting small molecules solubilities on endpoint devices using deep ensemble neural networksCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified