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 661–670 of 4891 papers

TitleStatusHype
Acc3D: Accelerating Single Image to 3D Diffusion Models via Edge Consistency Guided Score Distillation—0
Temporal-Spatial Attention Network (TSAN) for DoS Attack Detection in Network Traffic—0
The Morphology-Control Trade-Off: Insights into Soft Robotic Efficiency—0
Learning Distributions of Complex Fluid Simulations with Diffusion Graph Networks—0
A Comprehensive Survey on Architectural Advances in Deep CNNs: Challenges, Applications, and Emerging Research Directions—0
Enhanced Vascular Flow Simulations in Aortic Aneurysm via Physics-Informed Neural Networks and Deep Operator Networks—0
Joint Design of Radar Receive Filter and Unimodular ISAC Waveform with Sidelobe Level Control—0
Online federated learning framework for classification—0
EdgeRegNet: Edge Feature-based Multimodal Registration Network between Images and LiDAR Point CloudsCode1
Pruning-Based TinyML Optimization of Machine Learning Models for Anomaly Detection in Electric Vehicle Charging InfrastructureCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified