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 1151–1160 of 4891 papers

TitleStatusHype
Outlier-aware Tensor Robust Principal Component Analysis with Self-guided Data Augmentation—0
On the workflow, opportunities and challenges of developing foundation model in geophysics—0
Combining GCN Structural Learning with LLM Chemical Knowledge for or Enhanced Virtual Screening—0
Towards Robust LLMs: an Adversarial Robustness Measurement FrameworkCode0
Mixed Bernstein-Fourier Approximants for Optimal Trajectory Generation with Periodic Behavior—0
Data-Driven Surrogate Modeling Techniques to Predict the Effective Contact Area of Rough Surface Contact Problems—0
A Spatially-Aware Multiple Instance Learning Framework for Digital PathologyCode0
Precision Neural Network Quantization via Learnable Adaptive Modules—0
Improving Significant Wave Height Prediction Using Chronos Models—0
An Accelerated Camera 3DMA Framework for Efficient Urban GNSS Multipath Estimation—0
Show:102550
← PrevPage 116 of 490Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified