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 4191–4200 of 4891 papers

TitleStatusHype
SparseIDS: Learning Packet Sampling with Reinforcement LearningCode1
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
3-D Short-Range Imaging With Irregular MIMO Arrays Using NUFFT-Based Range Migration AlgorithmCode1
AMR Similarity Metrics from PrinciplesCode1
stream-learn -- open-source Python library for difficult data stream batch analysisCode1
SafeNet: An Assistive Solution to Assess Incoming Threats for Premises—0
Computing the Feedback Capacity of Finite State Channels using Reinforcement LearningCode0
Curvature Regularized Surface Reconstruction from Point Cloud—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified