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 461–470 of 4891 papers

TitleStatusHype
LighTDiff: Surgical Endoscopic Image Low-Light Enhancement with T-DiffusionCode1
DispFormer: Pretrained Transformer for Flexible Dispersion Curve Inversion from Global Synthesis to Regional ApplicationsCode1
A prediction and behavioural analysis of machine learning methods for modelling travel mode choiceCode1
DiMoSR: Feature Modulation via Multi-Branch Dilated Convolutions for Efficient Image Super-ResolutionCode1
Distance Encoding: Design Provably More Powerful Neural Networks for Graph Representation LearningCode1
DQO-MAP: Dual Quadrics Multi-Object mapping with Gaussian SplattingCode1
Dynamic-VLM: Simple Dynamic Visual Token Compression for VideoLLMCode1
Enhancing Fast Feed Forward Networks with Load Balancing and a Master Leaf NodeCode1
DeepZero: Scaling up Zeroth-Order Optimization for Deep Model TrainingCode1
Deep Transfer Learning for Land Use and Land Cover Classification: A Comparative StudyCode1
Show:102550
← PrevPage 47 of 490Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified