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 431–440 of 4891 papers

TitleStatusHype
When Cloud Removal Meets Diffusion Model in Remote Sensing—0
Enhanced Data-driven Topology Design Methodology with Multi-level Mesh and Correlation-based Mutation for Stress-related Multi-objective Optimization—0
DistilQwen2.5: Industrial Practices of Training Distilled Open Lightweight Language Models—0
Exploring _0 Sparsification for Inference-free Sparse RetrieversCode1
Neural ATTF: A Scalable Solution to Lifelong Multi-Agent Path Planning—0
STARS: Sparse Learning Correlation Filter with Spatio-temporal Regularization and Super-resolution Reconstruction for Thermal Infrared Target Tracking—0
SMTT: Novel Structured Multi-task Tracking with Graph-Regularized Sparse Representation for Robust Thermal Infrared Target Tracking—0
ReasoningV: Efficient Verilog Code Generation with Adaptive Hybrid Reasoning ModelCode0
VM-BHINet:Vision Mamba Bimanual Hand Interaction Network for 3D Interacting Hand Mesh Recovery From a Single RGB Image—0
Mixed-Precision Conjugate Gradient Solvers with RL-Driven Precision Tuning—0
Show:102550
← PrevPage 44 of 490Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified