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 1971–1980 of 4891 papers

TitleStatusHype
Multi-Fidelity Bayesian Neural Network for Uncertainty Quantification in Transonic Aerodynamic Loads—0
Igea: a Decoder-Only Language Model for Biomedical Text Generation in Italian—0
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
Waterfall: Framework for Robust and Scalable Text Watermarking and Provenance for LLMsCode1
DASS: Distilled Audio State Space Models Are Stronger and More Duration-Scalable LearnersCode1
QET: Enhancing Quantized LLM Parameters and KV cache Compression through Element Substitution and Residual Clustering—0
Prediction-Free Coordinated Dispatch of Microgrid: A Data-Driven Online Optimization Approach—0
Mixture of A Million ExpertsCode2
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified