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 3011–3020 of 4891 papers

TitleStatusHype
Posterior Sampling with Delayed Feedback for Reinforcement Learning with Linear Function Approximation—0
Med-DANet V2: A Flexible Dynamic Architecture for Efficient Medical Volumetric Segmentation—0
Triplet Attention Transformer for Spatiotemporal Predictive Learning—0
Efficient safe learning for controller tuning with experimental validation—0
Proactive Emergency Collision Avoidance for Automated Driving in Highway Scenarios—0
Image Prior and Posterior Conditional Probability Representation for Efficient Damage Assessment—0
Explainable Gated Bayesian Recurrent Neural Network for Non-Markov State Estimation—0
Model Input-Output Configuration Search with Embedded Feature Selection for Sensor Time-series and Image ClassificationCode0
Orchestration of Emulator Assisted Mobile Edge Tuning for AI Foundation Models: A Multi-Agent Deep Reinforcement Learning Approach—0
The Significance of Machine Learning in Clinical Disease Diagnosis: A Review—0
Show:102550
← PrevPage 302 of 490Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified