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 3601–3625 of 4891 papers

TitleStatusHype
Minimax Optimal Goodness-of-Fit Testing with Kernel Stein Discrepancy—0
Minimax Optimal Kernel Two-Sample Tests with Random Features—0
Minimax Optimal Reinforcement Learning with Quasi-Optimism—0
Minimizing the Number of Matching Queries for Object Retrieval—0
Mining Combined Causes in Large Data Sets—0
Mining Sub-Interval Relationships In Time Series Data—0
Mining Truck Platooning Patterns Through Massive Trajectory Data—0
MINT: Mutual Information based Transductive Feature Selection for Genetic Trait Prediction—0
MISO hierarchical inference engine satisfying the law of importation with aggregation functions—0
MissForest - nonparametric missing value imputation for mixed-type data—0
Mitigating Data Consistency Induced Discrepancy in Cascaded Diffusion Models for Sparse-view CT Reconstruction—0
Mitigating Hallucination with ZeroG: An Advanced Knowledge Management Engine—0
Mitigating loss of variance in ensemble data assimilation: machine learning-based and distance-free localizations for better covariance estimation—0
Mitigating Stop-and-Go Traffic Congestion with Operator Learning—0
MIX : a Multi-task Learning Approach to Solve Open-Domain Question Answering—0
Mixed Bernstein-Fourier Approximants for Optimal Trajectory Generation with Periodic Behavior—0
Mixed-gradients Distributed Filtered Reference Least Mean Square Algorithm -- A Robust Distributed Multichannel Active Noise Control Algorithm—0
Mixed Integer Linear Programming for Active Contact Selection in Deep Brain Stimulation—0
Mixed-Precision Conjugate Gradient Solvers with RL-Driven Precision Tuning—0
Mixed-Precision Federated Learning via Multi-Precision Over-The-Air Aggregation—0
MIXPINN: Mixed-Material Simulations by Physics-Informed Neural Network—0
Mix-QSAM: Mixed-Precision Quantization of the Segment Anything Model—0
ML-based identification of the interface regions for coupling local and nonlocal models—0
MLICv2: Enhanced Multi-Reference Entropy Modeling for Learned Image Compression—0
MobileDiffusion: Instant Text-to-Image Generation on Mobile Devices—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified