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 2291–2300 of 4891 papers

TitleStatusHype
Hawk: Accurate and Fast Privacy-Preserving Machine Learning Using Secure Lookup Table Computation—0
Grappa -- A Machine Learned Molecular Mechanics Force FieldCode2
Benchmarking Video Frame Interpolation—0
ModeTv2: GPU-accelerated Motion Decomposition Transformer for Pairwise Optimization in Medical Image RegistrationCode1
Understanding the Functional Roles of Modelling Components in Spiking Neural Networks—0
Stochastic parameter reduced-order model based on hybrid machine learning approaches—0
Crypto Inverse-Power Options and Fractional Stochastic Volatility—0
Salience DETR: Enhancing Detection Transformer with Hierarchical Salience Filtering RefinementCode3
A Unified Module for Accelerating STABLE-DIFFUSION: LCM-LORA—0
Fast and Unified Path Gradient Estimators for Normalizing Flows—0
Show:102550
← PrevPage 230 of 490Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified