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 3271–3280 of 4891 papers

TitleStatusHype
Frequency of Interest-based Noise Attenuation Method to Improve Anomaly Detection Performance—0
gSuite: A Flexible and Framework Independent Benchmark Suite for Graph Neural Network Inference on GPUs—0
Graph sampling for node embedding—0
Accelerate Three-Dimensional Generative Adversarial Networks Using Fast Algorithm—0
Phenomenological Model of Superconducting Optoelectronic Loop Neurons—0
RibSeg v2: A Large-scale Benchmark for Rib Labeling and Anatomical Centerline ExtractionCode0
A Novel Membership Inference Attack against Dynamic Neural Networks by Utilizing Policy Networks Information—0
TransVisDrone: Spatio-Temporal Transformer for Vision-based Drone-to-Drone Detection in Aerial VideosCode1
Dynamics-aware Adversarial Attack of Adaptive Neural NetworksCode0
An Efficient FPGA Accelerator for Point Cloud—0
Show:102550
← PrevPage 328 of 490Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified