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 1941–1950 of 4891 papers

TitleStatusHype
PADRe: A Unifying Polynomial Attention Drop-in Replacement for Efficient Vision Transformer—0
RIMformer: An End-to-End Transformer for FMCW Radar Interference Mitigation—0
A Benchmark for Fairness-Aware Graph Learning—0
Zero-Shot Adaptation for Approximate Posterior Sampling of Diffusion Models in Inverse ProblemsCode0
QVD: Post-training Quantization for Video Diffusion Models—0
On Machine Learning Approaches for Protein-Ligand Binding Affinity Prediction—0
Enhancing Stochastic Optimization for Statistical Efficiency Using ROOT-SGD with Diminishing Stepsize—0
DUCPS: Deep Unfolding the Cauchy Proximal Splitting Algorithm for B-Lines Quantification in Lung Ultrasound Images—0
Deep Learning Evidence for Global Optimality of Gerver's SofaCode0
TCM-FTP: Fine-Tuning Large Language Models for Herbal Prescription Prediction—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified