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 2151–2160 of 4891 papers

TitleStatusHype
Enhancing CNN Classification with Lamarckian Memetic Algorithms and Local Search—0
Causal Inference based Transfer Learning with LLMs: An Efficient Framework for Industrial RUL Prediction—0
Causal Generative Domain Adaptation Networks—0
A new graph-based surrogate model for rapid prediction of crashworthiness performance of vehicle panel components—0
A Deeper Look at 3D Shape Classifiers—0
Accurate and scalable exchange-correlation with deep learning—0
Pose Magic: Efficient and Temporally Consistent Human Pose Estimation with a Hybrid Mamba-GCN Network—0
GNN-SKAN: Harnessing the Power of SwallowKAN to Advance Molecular Representation Learning with GNNs—0
Enhanced Textual Feature Extraction for Visual Question Answering: A Simple Convolutional Approach—0
Enhanced Vascular Flow Simulations in Aortic Aneurysm via Physics-Informed Neural Networks and Deep Operator Networks—0
Show:102550
← PrevPage 216 of 490Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified