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 4051–4075 of 4891 papers

TitleStatusHype
Global-and-Local Relative Position Embedding for Unsupervised Video Summarization—0
Graph signal processing for machine learning: A review and new perspectives—0
SynergicLearning: Neural Network-Based Feature Extraction for Highly-Accurate Hyperdimensional Learning—0
Prediction of hierarchical time series using structured regularization and its application to artificial neural networks—0
Fully Dynamic Inference with Deep Neural Networks—0
Intelligent Optimization of Diversified Community Prevention of COVID-19 using Traditional Chinese Medicine—0
Corner Proposal Network for Anchor-free, Two-stage Object DetectionCode1
Resource Allocation via Model-Free Deep Learning in Free Space Optical Communications—0
Oblique Predictive Clustering TreesCode0
Langevin Monte Carlo: random coordinate descent and variance reduction—0
GP-Aligner: Unsupervised Non-rigid Groupwise Point Set Registration Based On Optimized Group Latent Descriptor—0
BabyAI 1.1Code1
A Novel Mobility Model to Support the Routing of Mobile Energy Resources—0
Privacy Preserving Visual SLAM—0
Supervised clustering of high dimensional data using regularized mixture modeling—0
Backpropagated Gradient Representations for Anomaly DetectionCode1
Transferred Energy Management Strategies for Hybrid Electric Vehicles Based on Driving Conditions Recognition—0
Approximate XVA for European claims—0
Graph Neural Networks for Scalable Radio Resource Management: Architecture Design and Theoretical AnalysisCode1
Allpass Feedback Delay Networks—0
State Space Expectation Propagation: Efficient Inference Schemes for Temporal Gaussian ProcessesCode1
VINNAS: Variational Inference-based Neural Network Architecture Search—0
A Computational Separation between Private Learning and Online Learning—0
Dynamic Group Convolution for Accelerating Convolutional Neural NetworksCode1
Optimization from Structured Samples for Coverage Functions—0
Show:102550
← PrevPage 163 of 196Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified