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 3741–3750 of 4891 papers

TitleStatusHype
Bayesian Sequential Stacking Algorithm for Concurrently Designing Molecules and Synthetic Reaction Networks—0
Bayesian Spatial Field Reconstruction with Unknown Distortions in Sensor Networks—0
Bayesian Structural Model Updating with Multimodal Variational Autoencoder—0
Bayesian Structure Learning by Recursive Bootstrap—0
Bayesian Triplet Loss: Uncertainty Quantification in Image Retrieval—0
BCSSN: Bi-direction Compact Spatial Separable Network for Collision Avoidance in Autonomous Driving—0
BeamLearning: an end-to-end Deep Learning approach for the angular localization of sound sources using raw multichannel acoustic pressure data—0
Bee-yond the Plateau: Training QNNs with Swarm Algorithms—0
Benchmark Evaluation of Image Fusion algorithms for Smartphone Camera Capture—0
Benchmarking and In-depth Performance Study of Large Language Models on Habana Gaudi Processors—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified