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 1751–1760 of 4891 papers

TitleStatusHype
Comprehensive Survey of Model Compression and Speed up for Vision Transformers—0
Compositionally-Warped Gaussian Processes—0
Composite Marginal Likelihood Methods for Random Utility Models—0
Composite Gaussian Processes Flows for Learning Discontinuous Multimodal Policies—0
A Point-Based Approach to Efficient LiDAR Multi-Task Perception—0
Composite Event Recognition for Maritime Monitoring—0
Composing MPC with LQR and Neural Network for Amortized Efficiency and Stable Control—0
A Physics-informed machine learning model for time-dependent wave runup prediction—0
Advancing Semantic Caching for LLMs with Domain-Specific Embeddings and Synthetic Data—0
Advancing Physics Data Analysis through Machine Learning and Physics-Informed Neural Networks—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified