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

Showing 561570 of 4891 papers

TitleStatusHype
Heat flux for semi-local machine-learning potentialsCode1
Marching-Primitives: Shape Abstraction from Signed Distance FunctionCode1
SIESTA: Efficient Online Continual Learning with SleepCode1
Robust Mode Connectivity-Oriented Adversarial Defense: Enhancing Neural Network Robustness Against Diversified _p AttacksCode1
Dynamic Clustering and Cluster Contrastive Learning for Unsupervised Person Re-identificationCode1
Resurrecting Recurrent Neural Networks for Long SequencesCode1
Scaling Up 3D Kernels with Bayesian Frequency Re-parameterization for Medical Image SegmentationCode1
DeepSeeColor: Realtime Adaptive Color Correction for Autonomous Underwater Vehicles via Deep Learning MethodsCode1
LightCTS: A Lightweight Framework for Correlated Time Series ForecastingCode1
Local Causal Discovery for Estimating Causal EffectsCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05Unverified