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 4031–4040 of 4891 papers

TitleStatusHype
Bayesian Triplet Loss: Uncertainty Quantification in Image Retrieval—0
Deep Convolutional Neural Networks: A survey of the foundations, selected improvements, and some current applications—0
RISE-SLAM: A Resource-aware Inverse Schmidt Estimator for SLAM—0
Accelerating Text Mining Using Domain-Specific Stop Word Lists—0
Switching Device-Cognizant Sequential Distribution System Restoration—0
Real-Time Radio Technology and Modulation Classification via an LSTM Auto-Encoder—0
Fast and Scalable Earth Texture Synthesis using Spatially Assembled Generative Adversarial Neural Networks—0
Support estimation in high-dimensional heteroscedastic mean regression—0
Transfer Function Analysis and Implementation of Active Disturbance Rejection Control—0
Non-Iterative Blind Calibration of Nested Arrays with Asymptotically Optimal Weighting—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified