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 4101–4125 of 4891 papers

TitleStatusHype
DIET-SNN: Direct Input Encoding With Leakage and Threshold Optimization in Deep Spiking Neural Networks—0
Pseudoinverse Graph Convolutional Networks: Fast Filters Tailored for Large Eigengaps of Dense Graphs and HypergraphsCode0
Hardware Accelerator for Adversarial Attacks on Deep Learning Neural Networks—0
Global-and-Local Relative Position Embedding for Unsupervised Video Summarization—0
Graph signal processing for machine learning: A review and new perspectives—0
Prediction of hierarchical time series using structured regularization and its application to artificial neural networks—0
SynergicLearning: Neural Network-Based Feature Extraction for Highly-Accurate Hyperdimensional Learning—0
Fully Dynamic Inference with Deep Neural Networks—0
Intelligent Optimization of Diversified Community Prevention of COVID-19 using Traditional Chinese Medicine—0
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
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
Transferred Energy Management Strategies for Hybrid Electric Vehicles Based on Driving Conditions Recognition—0
Approximate XVA for European claims—0
Allpass Feedback Delay Networks—0
VINNAS: Variational Inference-based Neural Network Architecture Search—0
A Computational Separation between Private Learning and Online Learning—0
Optimization from Structured Samples for Coverage Functions—0
Meta-Learning Divergences of Variational Inference—0
Local Grid Rendering Networks for 3D Object Detection in Point Clouds—0
High Dimensional Bayesian Optimization Assisted by Principal Component AnalysisCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified