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 3561–3570 of 4891 papers

TitleStatusHype
Reconstructing Compact Building Models from Point Clouds Using Deep Implicit FieldsCode1
A Deep Reinforcement Learning Approach for Solving the Traveling Salesman Problem with DroneCode1
Data-Driven Outage Restoration Time Prediction via Transfer Learning with Cluster Ensembles—0
Robust Data-Driven Linear Power Flow Model with Probability Constrained Worst-Case Errors—0
StyleSwin: Transformer-based GAN for High-resolution Image GenerationCode1
MISO hierarchical inference engine satisfying the law of importation with aggregation functions—0
GPU optimization of the 3D Scale-invariant Feature Transform Algorithm and a Novel BRIEF-inspired 3D Fast DescriptorCode1
Scheduling HVAC loads to promote renewable generation integration with a learning-based joint chance-constrained approach—0
Dynamics-aware Adversarial Attack of 3D Sparse Convolution NetworkCode0
AFDetV2: Rethinking the Necessity of the Second Stage for Object Detection from Point Clouds—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified