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 421–430 of 4891 papers

TitleStatusHype
InceptionMamba: An Efficient Hybrid Network with Large Band Convolution and Bottleneck MambaCode1
Real-Time Machine-Learning-Based Optimization Using Input Convex Long Short-Term Memory NetworkCode1
Fast TreeSHAP: Accelerating SHAP Value Computation for TreesCode1
FedSSA: Semantic Similarity-based Aggregation for Efficient Model-Heterogeneous Personalized Federated LearningCode1
An Extensible Benchmark Suite for Learning to Simulate Physical SystemsCode1
ACEnet: Anatomical Context-Encoding Network for Neuroanatomy SegmentationCode1
A Deep Reinforcement Learning Approach for Solving the Traveling Salesman Problem with DroneCode1
A machine learning-based viscoelastic-viscoplastic model for epoxy nanocomposites with moisture contentCode1
Beyond The Rainbow: High Performance Deep Reinforcement Learning on a Desktop PCCode1
Fast Kernel Scene FlowCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified