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 3091–3100 of 4891 papers

TitleStatusHype
Active Semi-Supervised Learning by Exploring Per-Sample Uncertainty and Consistency—0
BiFormer: Vision Transformer with Bi-Level Routing AttentionCode2
Interpretable Ensembles of Hyper-Rectangles as Base ModelsCode0
Optimal Sampling Designs for Multi-dimensional Streaming Time Series with Application to Power Grid Sensor Data—0
Gradient-Descent Based Optimization of Constant Envelope OFDM Waveforms—0
Dynamic Clustering and Cluster Contrastive Learning for Unsupervised Person Re-identificationCode1
Importance Filtering with Risk Models for Complex Driving Situations—0
NeurEPDiff: Neural Operators to Predict Geodesics in Deformation Spaces—0
BCSSN: Bi-direction Compact Spatial Separable Network for Collision Avoidance in Autonomous Driving—0
A Convergent Single-Loop Algorithm for Relaxation of Gromov-Wasserstein in Graph DataCode4
Show:102550
← PrevPage 310 of 490Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified