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 2841–2850 of 4891 papers

TitleStatusHype
Split Learning for Distributed Collaborative Training of Deep Learning Models in Health Informatics—0
Square One Bias in NLP: Towards a Multi-Dimensional Exploration of the Research Manifold—0
Square One Bias in NLP: Towards a Multi-Dimensional Exploration of the Research Manifold—0
SSA-CNN: Semantic Self-Attention CNN for Pedestrian Detection—0
SSD-Poser: Avatar Pose Estimation with State Space Duality from Sparse Observations—0
SSF-PAN: Semantic Scene Flow-Based Perception for Autonomous Navigation in Traffic Scenarios—0
SSL-NBV: A Self-Supervised-Learning-Based Next-Best-View algorithm for Efficient 3D Plant Reconstruction by a Robot—0
SSN: Stockwell Scattering Network for SAR Image Change Detection—0
Randomized Sharpness-Aware Training for Boosting Computational Efficiency in Deep Learning—0
Stability Constrained Mobile Manipulation Planning on Rough Terrain—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified