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 1401–1410 of 4891 papers

TitleStatusHype
Umbrella Reinforcement Learning -- computationally efficient tool for hard non-linear problemsCode0
Learning Pore-scale Multi-phase Flow from Experimental Data with Graph Neural Network—0
Beyond Training: Dynamic Token Merging for Zero-Shot Video Understanding—0
Quantum Attention for Vision Transformers in High Energy Physics—0
When Precision Meets Position: BFloat16 Breaks Down RoPE in Long-Context TrainingCode3
Improving Low-Fidelity Models of Li-ion Batteries via Hybrid Sparse Identification of Nonlinear Dynamics—0
Moving Horizon Estimation for Simultaneous Localization and Mapping with Robust Estimation Error Bounds—0
LaVida Drive: Vision-Text Interaction VLM for Autonomous Driving with Token Selection, Recovery and Enhancement—0
Real-Time Energy-Optimal Path Planning for Electric Vehicles—0
High-Throughput Blind Co-Channel Interference Cancellation for Edge Devices Using Depthwise Separable Convolutions, Quantization, and Pruning—0
Show:102550
← PrevPage 141 of 490Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified