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 726–750 of 4891 papers

TitleStatusHype
MAGIC: Learning Macro-Actions for Online POMDP PlanningCode1
Multi\^2OIE: Multilingual Open Information Extraction Based on Multi-Head Attention with BERTCode1
A Simple Local Minimal Intensity Prior and An Improved Algorithm for Blind Image DeblurringCode1
Federated Bayesian Optimization via Thompson SamplingCode1
Efficient and Compact Convolutional Neural Network Architectures for Non-temporal Real-time Fire DetectionCode1
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
MATS: An Interpretable Trajectory Forecasting Representation for Planning and ControlCode1
YOLObile: Real-Time Object Detection on Mobile Devices via Compression-Compilation Co-DesignCode1
Distance Encoding: Design Provably More Powerful Neural Networks for Graph Representation LearningCode1
Joint Design of RF and gradient waveforms via auto-differentiation for 3D tailored excitation in MRICode1
Revisiting Temporal Modeling for Video Super-resolutionCode1
Improving Multispectral Pedestrian Detection by Addressing Modality Imbalance ProblemsCode1
Corner Proposal Network for Anchor-free, Two-stage Object DetectionCode1
BabyAI 1.1Code1
Backpropagated Gradient Representations for Anomaly DetectionCode1
Graph Neural Networks for Scalable Radio Resource Management: Architecture Design and Theoretical AnalysisCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified