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 1211–1220 of 4891 papers

TitleStatusHype
Inference Scaling vs Reasoning: An Empirical Analysis of Compute-Optimal LLM Problem-SolvingCode0
Theory of Mixture-of-Experts for Mobile Edge Computing—0
Ethics and Technical Aspects of Generative AI Models in Digital Content Creation—0
Less is More: Towards Green Code Large Language Models via Unified Structural Pruning—0
Explainable AI for Multivariate Time Series Pattern Exploration: Latent Space Visual Analytics with Temporal Fusion Transformer and Variational Autoencoders in Power Grid Event Diagnosis—0
DOLLAR: Few-Step Video Generation via Distillation and Latent Reward Optimization—0
VLM-RL: A Unified Vision Language Models and Reinforcement Learning Framework for Safe Autonomous Driving—0
Adaptable and Precise: Enterprise-Scenario LLM Function-Calling Capability Training Pipeline—0
Federated Learning for Coronary Artery Plaque Detection in Atherosclerosis Using IVUS Imaging: A Multi-Hospital Collaboration—0
Consistent Human Image and Video Generation with Spatially Conditioned DiffusionCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified