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 4211–4220 of 4891 papers

TitleStatusHype
Distributed Sketching Methods for Privacy Preserving Regression—0
Fast Fair Regression via Efficient Approximations of Mutual InformationCode0
Multi-Vehicle Routing Problems with Soft Time Windows: A Multi-Agent Reinforcement Learning Approach—0
Development of modeling and control strategies for an approximated Gaussian process—0
Learning Flat Latent Manifolds with VAEs—0
Improving the affordability of robustness training for DNNs—0
Object-Adaptive LSTM Network for Real-time Visual Tracking with Adversarial Data Augmentation—0
Distance Metric Learning for Graph Structured DataCode0
A Hybrid Two-layer Feature Selection Method Using GeneticAlgorithm and Elastic Net—0
Computing the Feedback Capacity of Finite State Channels using Reinforcement LearningCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified