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 2471–2480 of 4891 papers

TitleStatusHype
Mamba Hawkes Process—0
The Solution for the AIGC Inference Performance Optimization Competition—0
LMSeg: A deep graph message-passing network for efficient and accurate semantic segmentation of large-scale 3D landscape meshes—0
Prediction-Free Coordinated Dispatch of Microgrid: A Data-Driven Online Optimization Approach—0
QET: Enhancing Quantized LLM Parameters and KV cache Compression through Element Substitution and Residual Clustering—0
Plant Doctor: A hybrid machine learning and image segmentation software to quantify plant damage in video footage—0
OSPC: Artificial VLM Features for Hateful Meme Detection—0
Improving Conversational Abilities of Quantized Large Language Models via Direct Preference Alignment—0
Feature-Specific Coefficients of Determination in Tree Ensembles—0
On the Robustness of Graph Reduction Against GNN Backdoor—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified