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 731–740 of 4891 papers

TitleStatusHype
Decomposing non-stationary signals with time-varying wave-shape functionsCode1
Correlation Filters for Unmanned Aerial Vehicle-Based Aerial Tracking: A Review and Experimental EvaluationCode1
Glance and Focus: a Dynamic Approach to Reducing Spatial Redundancy in Image ClassificationCode1
Robust Behavioral Cloning for Autonomous Vehicles using End-to-End Imitation LearningCode1
A Transformer-based Framework for Multivariate Time Series Representation LearningCode1
DCT-SNN: Using DCT to Distribute Spatial Information over Time for Learning Low-Latency Spiking Neural NetworksCode1
Self-grouping Convolutional Neural NetworksCode1
Multi-Relational Embedding for Knowledge Graph Representation and AnalysisCode1
Multiscale Context-Aware Ensemble Deep KELM for Efficient Hyperspectral Image ClassificationCode1
Multi^2OIE: Multilingual Open Information Extraction Based on Multi-Head Attention with BERTCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified