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 3301–3310 of 4891 papers

TitleStatusHype
Where Should I Spend My FLOPS? Efficiency Evaluations of Visual Pre-training Methods—0
ERNIE-ViL 2.0: Multi-view Contrastive Learning for Image-Text Pre-training—0
Low-Dose CT Using Denoising Diffusion Probabilistic Model for 20 Speedup—0
Offline Reinforcement Learning via High-Fidelity Generative Behavior ModelingCode1
Prompt-driven efficient Open-set Semi-supervised Learning—0
Less is More: Rethinking Few-Shot Learning and Recurrent Neural Nets—0
DCE: Offline Reinforcement Learning With Double Conservative Estimates—0
Towards Multimodal Multitask Scene Understanding Models for Indoor Mobile Agents—0
Sampling Constrained Continuous Probability Distributions: A Review—0
Machine Learning for Improved Gas Network Models in Coordinated Energy Systems—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified