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 71–80 of 4891 papers

TitleStatusHype
Accurate and scalable exchange-correlation with deep learning—0
A Comprehensive Survey on Deep Learning Solutions for 3D Flood Mapping—0
Finding Optimal Kernel Size and Dimension in Convolutional Neural Networks An Architecture Optimization Approach—0
Mitigating loss of variance in ensemble data assimilation: machine learning-based and distance-free localizations for better covariance estimation—0
Improving Prostate Gland Segmenting Using Transformer based Architectures—0
Hybrid Polynomial Zonotopes: A Set Representation for Reachability Analysis in Hybrid Nonaffine Systems—0
A Memetic Walrus Algorithm with Expert-guided Strategy for Adaptive Curriculum Sequencing—0
GreedyPrune: Retenting Critical Visual Token Set for Large Vision Language Models—0
Sparse Convolutional Recurrent Learning for Efficient Event-based Neuromorphic Object Detection—0
IKDiffuser: A Generative Inverse Kinematics Solver for Multi-arm Robots via Diffusion Model—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified