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 2681–2690 of 4891 papers

TitleStatusHype
Comprehensive Survey of Model Compression and Speed up for Vision Transformers—0
HSIDMamba: Exploring Bidirectional State-Space Models for Hyperspectral Denoising—0
Node Similarities under Random Projections: Limits and Pathological Cases—0
Post-Training Network Compression for 3D Medical Image Segmentation: Reducing Computational Efforts via Tucker DecompositionCode0
Proof-of-Learning with Incentive Security—0
Minimax Optimal Goodness-of-Fit Testing with Kernel Stein Discrepancy—0
Generalized Population-Based Training for Hyperparameter Optimization in Reinforcement LearningCode0
Diffusion-Based Joint Temperature and Precipitation Emulation of Earth System Models—0
BERT-LSH: Reducing Absolute Compute For AttentionCode0
Precoder Design for User-Centric Network Massive MIMO with Matrix Manifold Optimization—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified