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 3921–3930 of 4891 papers

TitleStatusHype
Self-Supervised Learning of Compressed Video Representations—0
Bayesian Neural Networks with Variance Propagation for Uncertainty Evaluation—0
A Simple Unified Information Regularization Framework for Multi-Source Domain Adaptation—0
Optimizing Quantized Neural Networks with Natural Gradient—0
DIET-SNN: A Low-Latency Spiking Neural Network with Direct Input Encoding & Leakage and Threshold Optimization—0
Balancing training time vs. performance with Bayesian Early Pruning—0
Adjusted chi-square test for degree-corrected block modelsCode0
A Doubly Stochastic Simulator with Applications in Arrivals Modeling and SimulationCode0
A non-alternating graph hashing algorithm for large scale image searchCode0
StainNet: a fast and robust stain normalization networkCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified