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 2831–2840 of 4891 papers

TitleStatusHype
Advancing Brain-Computer Interface System Performance in Hand Trajectory Estimation with NeuroKinect—0
Partially Observable Multi-Agent Reinforcement Learning with Information Sharing—0
SCQPTH: an efficient differentiable splitting method for convex quadratic programmingCode0
Exploring Winograd Convolution for Cost-effective Neural Network Fault Tolerance—0
FedPop: Federated Population-based Hyperparameter TuningCode1
Fast Uncertainty Quantification of Spent Nuclear Fuel with Neural Networks—0
Deep Neural Operator Driven Real Time Inference for Nuclear Systems to Enable Digital Twin Solutions—0
Shortcut-V2V: Compression Framework for Video-to-Video Translation based on Temporal Redundancy Reduction—0
SEDA: Self-Ensembling ViT with Defensive Distillation and Adversarial Training for robust Chest X-rays ClassificationCode0
Probabilistic MIMO U-Net: Efficient and Accurate Uncertainty Estimation for Pixel-wise RegressionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified