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 3451–3460 of 4891 papers

TitleStatusHype
A* Lasso for Learning a Sparse Bayesian Network Structure for Continuous Variables—0
Algebraic-Combinatorial Methods for Low-Rank Matrix Completion with Application to Athletic Performance Prediction—0
Algorithms of Real-Time Navigation and Control of Autonomous Unmanned Vehicles—0
Algorithm Unrolling for Massive Access via Deep Neural Network with Theoretical Guarantee—0
A lightweight deep learning pipeline with DRDA-Net and MobileNet for breast cancer classification—0
A Lightweight Feature Fusion Architecture For Resource-Constrained Crowd Counting—0
A Linearized Alternating Direction Multiplier Method for Federated Matrix Completion Problems—0
All Birds with One Stone: Multi-task Learning for Inference with One Forward Pass—0
Allpass Feedback Delay Networks—0
A Low-Complexity Plug-and-Play Deep Learning Model for Massive MIMO Precoding Across Sites—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified