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 771–780 of 4891 papers

TitleStatusHype
GNNMerge: Merging of GNN Models Without Accessing Training DataCode0
Small but Mighty: Enhancing Time Series Forecasting with Lightweight LLMsCode1
TrafficKAN-GCN: Graph Convolutional-based Kolmogorov-Arnold Network for Traffic Flow OptimizationCode0
DiRe-JAX: A JAX based Dimensionality Reduction Algorithm for Large-scale DataCode1
LREA: Low-Rank Efficient Attention on Modeling Long-Term User Behaviors for CTR Prediction—0
Accelerating Focal Search in Multi-Agent Path Finding with Tighter Lower BoundsCode0
DQO-MAP: Dual Quadrics Multi-Object mapping with Gaussian SplattingCode1
COMMA: Coordinate-aware Modulated Mamba Network for 3D Dispersed Vessel SegmentationCode0
Seeded Poisson Factorization: Leveraging domain knowledge to fit topic modelsCode0
Multi-Step Deep Koopman Network (MDK-Net) for Vehicle Control in Frenet Frame—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified