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 4131–4140 of 4891 papers

TitleStatusHype
Accelerating Deep Neuroevolution on Distributed FPGAs for Reinforcement Learning Problems—0
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
Efficient and Information-Preserving Future Frame Prediction and BeyondCode1
Multilingual Joint Fine-tuning of Transformer models for identifying Trolling, Aggression and Cyberbullying at TRAC 2020Code0
The 4th AI City Challenge—0
EfficientPose: Scalable single-person pose estimationCode1
Self-Organized Operational Neural Networks with Generative NeuronsCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified