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 4676–4700 of 4891 papers

TitleStatusHype
sHGCN: Simplified hyperbolic graph convolutional neural networksCode0
Quantum-inspired canonical correlation analysis for exponentially large dimensional dataCode0
Wafer2Spike: Spiking Neural Network for Wafer Map Pattern ClassificationCode0
SU-YOLO: Spiking Neural Network for Efficient Underwater Object DetectionCode0
SVNet: Where SO(3) Equivariance Meets Binarization on Point Cloud RepresentationCode0
Quantum Natural Stochastic Pairwise Coordinate DescentCode0
Quantum Optimization via Gradient-Based Hamiltonian DescentCode0
SHRIMP: Sparser Random Feature Models via Iterative Magnitude PruningCode0
FastNetCode0
Gaussian Mixture Reduction with Composite Transportation DivergenceCode0
A Unified Framework for Foreground and Anonymization Area Segmentation in CT and MRI DataCode0
Dimension-reduced Optimization of Multi-zone Thermostatically Controlled LoadsCode0
CoDiCast: Conditional Diffusion Model for Global Weather Prediction with Uncertainty QuantificationCode0
Fast Multi-grid Methods for Minimizing Curvature EnergyCode0
CNNtention: Can CNNs do better with Attention?Code0
CMML: Contextual Modulation Meta Learning for Cold-Start RecommendationCode0
Most Influential Subset Selection: Challenges, Promises, and BeyondCode0
FastLexRank: Efficient Lexical Ranking for Structuring Social Media PostsCode0
Analyzing Large Language Models for Classroom Discussion AssessmentCode0
Fast large-scale optimization by unifying stochastic gradient and quasi-Newton methodsCode0
PLUM: Improving Inference Efficiency By Leveraging Repetition-Sparsity Trade-OffCode0
Fast Incremental von Neumann Graph Entropy Computation: Theory, Algorithm, and ApplicationsCode0
Fast Fair Regression via Efficient Approximations of Mutual InformationCode0
Adapt and Diffuse: Sample-adaptive Reconstruction via Latent Diffusion ModelsCode0
FasterVideo: Efficient Online Joint Object Detection And TrackingCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified