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

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Showing 1621–1630 of 4891 papers

TitleStatusHype
Gridded Transformer Neural Processes for Large Unstructured Spatio-Temporal Data—0
Accelerating the discovery of low-energy structure configurations: a computational approach that integrates first-principles calculations, Monte Carlo sampling, and Machine Learning—0
Quantum-Inspired Portfolio Optimization In The QUBO Framework—0
Amortized Control of Continuous State Space Feynman-Kac Model for Irregular Time SeriesCode1
FLOPS: Forward Learning with OPtimal Sampling—0
Bayesian Estimation and Tuning-Free Rank Detection for Probability Mass Function Tensors—0
SPikE-SSM: A Sparse, Precise, and Efficient Spiking State Space Model for Long Sequences Learning—0
Residual Kolmogorov-Arnold Network for Enhanced Deep LearningCode3
Granular Ball Twin Support Vector MachineCode0
GARLIC: LLM-Guided Dynamic Progress Control with Hierarchical Weighted Graph for Long Document QA—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified