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 531–540 of 4891 papers

TitleStatusHype
AI Accelerator Survey and TrendsCode1
Detach-ROCKET: Sequential feature selection for time series classification with random convolutional kernelsCode1
DeepZero: Scaling up Zeroth-Order Optimization for Deep Model TrainingCode1
Deep Transfer Learning for Land Use and Land Cover Classification: A Comparative StudyCode1
DeformUX-Net: Exploring a 3D Foundation Backbone for Medical Image Segmentation with Depthwise Deformable ConvolutionCode1
Deep fiber clustering: Anatomically informed fiber clustering with self-supervised deep learning for fast and effective tractography parcellationCode1
Deep reinforcement learning for large-scale epidemic controlCode1
Activation-Informed Merging of Large Language ModelsCode1
DeepSeeColor: Realtime Adaptive Color Correction for Autonomous Underwater Vehicles via Deep Learning MethodsCode1
DeePoly: A High-Order Accuracy Scientific Machine Learning Framework for Function Approximation and Solving PDEsCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified