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 2011–2020 of 4891 papers

TitleStatusHype
An Integer Polynomial Programming Based Framework for Lifted MAP Inference—0
"FRAME: Forward Recursive Adaptive Model Extraction -- A Technique for Advance Feature Selection"—0
ExpertFlow: Optimized Expert Activation and Token Allocation for Efficient Mixture-of-Experts Inference—0
Frames and vertex-frequency representations in graph fractional Fourier domain—0
Expected Sliced Transport Plans—0
FRC-TOuNN: Topology Optimization of Continuous Fiber Reinforced Composites using Neural Network—0
Free-DyGS: Camera-Pose-Free Scene Reconstruction based on Gaussian Splatting for Dynamic Surgical Videos—0
Exp-Concavity of Proper Composite Losses—0
Exact Pareto Optimal Search for Multi-Task Learning and Multi-Criteria Decision-Making—0
Class-relevant Patch Embedding Selection for Few-Shot Image Classification—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified