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 121–130 of 4891 papers

TitleStatusHype
LightGNN: Simple Graph Neural Network for RecommendationCode2
REINFORCE++: A Simple and Efficient Approach for Aligning Large Language ModelsCode2
VideoLifter: Lifting Videos to 3D with Fast Hierarchical Stereo AlignmentCode2
A Light-Weight Framework for Open-Set Object Detection with Decoupled Feature Alignment in Joint SpaceCode2
The dark side of the forces: assessing non-conservative force models for atomistic machine learningCode2
Uni-AdaFocus: Spatial-temporal Dynamic Computation for Video RecognitionCode2
Object Detection using Event Camera: A MoE Heat Conduction based Detector and A New Benchmark DatasetCode2
Many-MobileNet: Multi-Model Augmentation for Robust Retinal Disease ClassificationCode2
2DMamba: Efficient State Space Model for Image Representation with Applications on Giga-Pixel Whole Slide Image ClassificationCode2
L4acados: Learning-based models for acados, applied to Gaussian process-based predictive controlCode2
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified