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 3031–3040 of 4891 papers

TitleStatusHype
ToolACE-R: Tool Learning with Adaptive Self-Refinement—0
Tool flank wear prediction using high-frequency machine data from industrial edge device—0
TopK Language Models—0
TorchMD-Net 2.0: Fast Neural Network Potentials for Molecular Simulations—0
TorchMD-NET: Equivariant Transformers for Neural Network based Molecular Potentials—0
To Repair or Not to Repair? Investigating the Importance of AB-Cycles for the State-of-the-Art TSP Heuristic EAX—0
Toward Efficient Automated Feature Engineering—0
Toward Fast and Accurate Neural Chinese Word Segmentation with Multi-Criteria Learning—0
An unsupervised, open-source workflow for 2D and 3D building mapping from airborne LiDAR data—0
Towards a physically realistic computationally efficient DVS pixel model—0
Show:102550
← PrevPage 304 of 490Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified