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 501–510 of 4891 papers

TitleStatusHype
STEI-PCN: an efficient pure convolutional network for traffic prediction via spatial-temporal encoding and inferring—0
Deep Reinforcement Learning for Day-to-day Dynamic Tolling in Tradable Credit Schemes—0
Representation Meets Optimization: Training PINNs and PIKANs for Gray-Box Discovery in Systems Pharmacology—0
V2V3D: View-to-View Denoised 3D Reconstruction for Light-Field Microscopy—0
Cluster-Driven Expert Pruning for Mixture-of-Experts Large Language ModelsCode0
Gradient-based Sample Selection for Faster Bayesian Optimization—0
Search-contempt: a hybrid MCTS algorithm for training AlphaZero-like engines with better computational efficiency—0
Probability Estimation and Scheduling Optimization for Battery Swap Stations via LRU-Enhanced Genetic Algorithm and Dual-Factor Decision SystemCode0
Regret Bounds for Robust Online Decision Making—0
Distilling Textual Priors from LLM to Efficient Image FusionCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified