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 3501–3510 of 4891 papers

TitleStatusHype
Merging Classification Predictions with Sequential Information for Lightweight Visual Place Recognition in Changing Environments—0
Efficient Meta-Learning for Continual Learning with Taylor Expansion Approximation—0
RotateCT: Knowledge Graph Embedding by Rotation and Coordinate Transformation in Complex Space—0
T2CI-GAN: Text to Compressed Image generation using Generative Adversarial Network—0
Sentiment Interpretable Logic Tensor Network for Aspect-Term Sentiment Analysis—0
Privacy-preserving Decentralized Federated Learning over Time-varying Communication Graph—0
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
Less is More: Rethinking Few-Shot Learning and Recurrent Neural Nets—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified