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 651–675 of 4891 papers

TitleStatusHype
HCMA-UNet: A Hybrid CNN-Mamba UNet with Axial Self-Attention for Efficient Breast Cancer SegmentationCode1
Automating DBSCAN via Deep Reinforcement LearningCode1
A machine learning-based viscoelastic-viscoplastic model for epoxy nanocomposites with moisture contentCode1
Bayesian inference for logistic models using Polya-Gamma latent variablesCode1
DeepOPF-V: Solving AC-OPF Problems EfficientlyCode1
Detach-ROCKET: Sequential feature selection for time series classification with random convolutional kernelsCode1
HiPart: Hierarchical Divisive Clustering ToolboxCode1
How Can We Be So Dense? The Benefits of Using Highly Sparse RepresentationsCode1
DualOpt: A Dual Divide-and-Optimize Algorithm for the Large-scale Traveling Salesman ProblemCode1
Efficient Neural Network Robustness Certification with General Activation FunctionsCode1
Hyper-CL: Conditioning Sentence Representations with HypernetworksCode1
DecoupleNet: A Lightweight Backbone Network With Efficient Feature Decoupling for Remote Sensing Visual TasksCode1
A Control-Oriented Simplified Single Particle Model with Grouped Parameter and Sensitivity Analysis for Lithium-Ion BatteriesCode1
A Variational Infinite Mixture for Probabilistic Inverse Dynamics LearningCode1
A Walsh Hadamard Derived Linear Vector Symbolic ArchitectureCode1
AMD-Hummingbird: Towards an Efficient Text-to-Video ModelCode1
Improving Computational Efficiency for Powered Descent Guidance via Transformer-based Tight Constraint PredictionCode1
BabyAI 1.1Code1
Improving Facade Parsing with Vision Transformers and Line IntegrationCode1
Backpropagated Gradient Representations for Anomaly DetectionCode1
Decoupling Spatio-Temporal Prediction: When Lightweight Large Models Meet Adaptive HypergraphsCode1
INSPIRE: Intensity and spatial information-based deformable image registrationCode1
DCT-SNN: Using DCT to Distribute Spatial Information over Time for Learning Low-Latency Spiking Neural NetworksCode1
Learning Enriched Features via Selective State Spaces Model for Efficient Image DeblurringCode1
Decomposing non-stationary signals with time-varying wave-shape functionsCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified