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 601–610 of 4891 papers

TitleStatusHype
Learning Hierarchical Image Segmentation For Recognition and By RecognitionCode1
Offline Reinforcement Learning via High-Fidelity Generative Behavior ModelingCode1
HiPart: Hierarchical Divisive Clustering ToolboxCode1
Tube-Based Zonotopic Data-Driven Predictive ControlCode1
Algorithmic Differentiation for Automated Modeling of Machine Learned Force FieldsCode1
Automating DBSCAN via Deep Reinforcement LearningCode1
On Fast Simulation of Dynamical System with Neural Vector Enhanced Numerical SolverCode1
Accelerated and interpretable oblique random survival forestsCode1
TransCL: Transformer Makes Strong and Flexible Compressive LearningCode1
SepLUT: Separable Image-adaptive Lookup Tables for Real-time Image EnhancementCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified