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 701–710 of 4891 papers

TitleStatusHype
CoLLMLight: Cooperative Large Language Model Agents for Network-Wide Traffic Signal ControlCode1
SDF-TopoNet: A Two-Stage Framework for Tubular Structure Segmentation via SDF Pre-training and Topology-Aware Fine-TuningCode0
Comparative Analysis of Advanced AI-based Object Detection Models for Pavement Marking Quality Assessment during Daytime—0
DynRsl-VLM: Enhancing Autonomous Driving Perception with Dynamic Resolution Vision-Language Models—0
Context-Aware Rule Mining Using a Dynamic Transformer-Based Framework—0
Spatio-temporal Fourier Transformer (StFT) for Long-term Dynamics Prediction—0
MMS-LLaMA: Efficient LLM-based Audio-Visual Speech Recognition with Minimal Multimodal Speech TokensCode1
FlowKac: An Efficient Neural Fokker-Planck solver using Temporal Normalizing flows and the Feynman Kac-FormulaCode0
Distance-Based Tree-Sliced Wasserstein DistanceCode0
Pathology Image Compression with Pre-trained Autoencoders—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified