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 2931–2940 of 4891 papers

TitleStatusHype
SweepEvGS: Event-Based 3D Gaussian Splatting for Macro and Micro Radiance Field Rendering from a Single Sweep—0
SWIFT: Scalable Wasserstein Factorization for Sparse Nonnegative Tensors—0
Switched Lyapunov Function based Controller Synthesis for Networked Control Systems: A Computationally Inexpensive Approach—0
Switching Device-Cognizant Sequential Distribution System Restoration—0
Symmetric Rank-One Quasi-Newton Methods for Deep Learning Using Cubic Regularization—0
Symmetry-invariant optimization in deep networks—0
SynCoTrain: A Dual Classifier PU-learning Framework for Synthesizability Prediction—0
SynergicLearning: Neural Network-Based Feature Extraction for Highly-Accurate Hyperdimensional Learning—0
Synthetic Training for Monocular Human Mesh Recovery—0
T2CI-GAN: Text to Compressed Image generation using Generative Adversarial Network—0
Show:102550
← PrevPage 294 of 490Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified