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 3301–3310 of 4891 papers

TitleStatusHype
Accelerate Three-Dimensional Generative Adversarial Networks Using Fast Algorithm—0
Accelerating Convolutional Neural Network Pruning via Spatial Aura Entropy—0
Accelerating Coordinate Descent via Active Set Selection for Device Activity Detection for Multi-Cell Massive Random Access—0
Accelerating Deep Neuroevolution on Distributed FPGAs for Reinforcement Learning Problems—0
Accelerating Inference of Retrieval-Augmented Generation via Sparse Context Selection—0
Accelerating IoV Intrusion Detection: Benchmarking GPU-Accelerated vs CPU-Based ML Libraries—0
Accelerating Machine Learning Algorithms with Adaptive Sampling—0
Accelerating Parallel Stochastic Gradient Descent via Non-blocking Mini-batches—0
Accelerating Sparse Graph Neural Networks with Tensor Core Optimization—0
Accelerating Spectral Clustering under Fairness Constraints—0
Show:102550
← PrevPage 331 of 490Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified