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 4171–4180 of 4891 papers

TitleStatusHype
Dynamic Shrinkage Priors for Large Time-varying Parameter Regressions using Scalable Markov Chain Monte Carlo Methods—0
The scalable Birth-Death MCMC Algorithm for Mixed Graphical Model Learning with Application to Genomic Data IntegrationCode0
Development of a skateboarding trick classifier using accelerometry and machine learningCode0
Mathematical foundations of stable RKHSs—0
Multilingual Joint Fine-tuning of Transformer models for identifying Trolling, Aggression and Cyberbullying at TRAC 2020Code0
The 4th AI City Challenge—0
Automatic exposure selection and fusion for high-dynamic-range photography via smartphones—0
Predicting Online Item-choice Behavior: A Shape-restricted Regression Perspective—0
Biophysically detailed mathematical models of multiscale cardiac active mechanicsCode0
Scaling Bayesian inference of mixed multinomial logit models to very large datasets—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified