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 1376–1400 of 4891 papers

TitleStatusHype
Dynamic Bi-Elman Attention Networks: A Dual-Directional Context-Aware Test-Time Learning for Text ClassificationCode0
Temporal Encoding Strategies for Energy Time Series PredictionCode0
Pruning-Based TinyML Optimization of Machine Learning Models for Anomaly Detection in Electric Vehicle Charging InfrastructureCode0
Online federated learning framework for classification—0
Machine learning surrogate models of many-body dispersion interactions in polymer melts—0
Enhanced Vascular Flow Simulations in Aortic Aneurysm via Physics-Informed Neural Networks and Deep Operator Networks—0
Learning Distributions of Complex Fluid Simulations with Diffusion Graph Networks—0
Modular Distributed Nonconvex Learning with Error Feedback—0
Variational Autoencoded Multivariate Spatial Fay-Herriot Models—0
Automatic MILP Model Construction for Multi-Robot Task Allocation and Scheduling Based on Large Language Models—0
Involution and BSConv Multi-Depth Distillation Network for Lightweight Image Super-Resolution—0
Towards Better Sample Efficiency in Multi-Agent Reinforcement Learning via Exploration—0
MIXPINN: Mixed-Material Simulations by Physics-Informed Neural Network—0
BlobCtrl: A Unified and Flexible Framework for Element-level Image Generation and Editing—0
A Linearized Alternating Direction Multiplier Method for Federated Matrix Completion Problems—0
Decouple to Reconstruct: High Quality UHD Restoration via Active Feature Disentanglement and Reversible Fusion—0
GFSNetwork: Differentiable Feature Selection via Gumbel-Sigmoid RelaxationCode0
Towards Scalable Foundation Model for Multi-modal and Hyperspectral Geospatial Data—0
Personalize Anything for Free with Diffusion Transformer—0
A new graph-based surrogate model for rapid prediction of crashworthiness performance of vehicle panel components—0
AI-Powered Automated Model Construction for Patient-Specific CFD Simulations of Aortic Flows—0
Data-Driven Approximation of Binary-State Network Reliability Function: Algorithm Selection and Reliability Thresholds for Large-Scale Systems—0
Applications of Large Language Model Reasoning in Feature Generation—0
Context-Aware Rule Mining Using a Dynamic Transformer-Based Framework—0
FlowKac: An Efficient Neural Fokker-Planck solver using Temporal Normalizing flows and the Feynman Kac-FormulaCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified