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 1961–1970 of 4891 papers

TitleStatusHype
SCASeg: Strip Cross-Attention for Efficient Semantic Segmentation—0
GMFlow: Global Motion-Guided Recurrent Flow for 6D Object Pose Estimation—0
SuperMat: Physically Consistent PBR Material Estimation at Interactive Rates—0
Enhancing Character-Level Understanding in LLMs through Token Internal Structure LearningCode0
Spatio-temporal Causal Learning for Streamflow Forecasting—0
Perceptually Optimized Super Resolution—0
Efficient Deployment of Transformer Models in Analog In-Memory Computing HardwareCode0
Flow Annealed Importance Sampling Bootstrap meets Differentiable Particle PhysicsCode0
Learning Optimal Lattice Vector Quantizers for End-to-end Neural Image Compression—0
Jaya R Package -- A Parameter-Free Solution for Advanced Single and Multi-Objective Optimization—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified