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 471–480 of 4891 papers

TitleStatusHype
From NeRFLiX to NeRFLiX++: A General NeRF-Agnostic Restorer ParadigmCode1
Dual Prototype Evolving for Test-Time Generalization of Vision-Language ModelsCode1
GPr-Net: Geometric Prototypical Network for Point Cloud Few-Shot LearningCode1
3D Human Pose and Shape Estimation via HybrIK-TransformerCode1
Federated Bayesian Optimization via Thompson SamplingCode1
Beyond The Rainbow: High Performance Deep Reinforcement Learning on a Desktop PCCode1
FedPop: Federated Population-based Hyperparameter TuningCode1
Meta-Sampler: Almost-Universal yet Task-Oriented Sampling for Point CloudsCode1
From Claims to Evidence: A Unified Framework and Critical Analysis of CNN vs. Transformer vs. Mamba in Medical Image SegmentationCode1
featsel: A framework for benchmarking of feature selection algorithms and cost functionsCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified