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 271–280 of 4891 papers

TitleStatusHype
Estimating Koopman operators with sketching to provably learn large scale dynamical systemsCode1
Event-based Video Reconstruction via Potential-assisted Spiking Neural NetworkCode1
Evidential Deep Learning: Enhancing Predictive Uncertainty Estimation for Earth System Science ApplicationsCode1
Equivariant Hypergraph Diffusion Neural OperatorsCode1
Bidirectional Recurrence for Cardiac Motion Tracking with Gaussian Process Latent CodingCode1
SE(3) Equivariant Graph Neural Networks with Complete Local FramesCode1
Beyond The Rainbow: High Performance Deep Reinforcement Learning on a Desktop PCCode1
Enhancing Medical Image Registration via Appearance Adjustment NetworksCode1
EViT: An Eagle Vision Transformer with Bi-Fovea Self-AttentionCode1
Energy-based physics-informed neural network for frictionless contact problems under large deformationCode1
Show:102550
← PrevPage 28 of 490Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified