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 3321–3330 of 4891 papers

TitleStatusHype
Optimization of Rocker-Bogie Mechanism using Heuristic Approaches—0
SVNet: Where SO(3) Equivariance Meets Binarization on Point Cloud RepresentationCode0
A Non-Parametric Bootstrap for Spectral Clustering—0
Hybrid Supervised and Reinforcement Learning for the Design and Optimization of Nanophotonic Structures—0
Non-iterative generation of an optimal mesh for a blade passage using deep reinforcement learning—0
Multitask Learning via Shared Features: Algorithms and Hardness—0
Tube-Based Zonotopic Data-Driven Predictive ControlCode1
Improving Out-of-Distribution Detection via Epistemic Uncertainty Adversarial Training—0
Optimistic Optimization of Gaussian Process Samples—0
Latent Similarity Identifies Important Functional Connections for Phenotype PredictionCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified