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 2241–2250 of 4891 papers

TitleStatusHype
A Point-Based Approach to Efficient LiDAR Multi-Task Perception—0
Proteus: Preserving Model Confidentiality during Graph OptimizationsCode0
Partial Large Kernel CNNs for Efficient Super-ResolutionCode2
Analytical results for uncertainty propagation through trained machine learning regression models—0
LongVQ: Long Sequence Modeling with Vector Quantization on Structured Memory—0
Stepwise Alignment for Constrained Language Model Policy OptimizationCode0
Comprehensive Survey of Model Compression and Speed up for Vision Transformers—0
Node Similarities under Random Projections: Limits and Pathological Cases—0
HSIDMamba: Exploring Bidirectional State-Space Models for Hyperspectral Denoising—0
Post-Training Network Compression for 3D Medical Image Segmentation: Reducing Computational Efforts via Tucker DecompositionCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified