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 2281–2290 of 4891 papers

TitleStatusHype
Rapid Parameter Estimation for Extreme Mass Ratio Inspirals Using Machine Learning—0
In-Situ Fine-Tuning of Wildlife Models in IoT-Enabled Camera Traps for Efficient Adaptation—0
GateAttentionPose: Enhancing Pose Estimation with Agent Attention and Improved Gated Convolutions—0
Mamba for Scalable and Efficient Personalized Recommendations—0
A Continual and Incremental Learning Approach for TinyML On-device Training Using Dataset Distillation and Model Size Adaption—0
The Weak Form Is Stronger Than You Think—0
Uncertainty Quantification in Seismic Inversion Through Integrated Importance Sampling and Ensemble Methods—0
CerviXpert: A Multi-Structural Convolutional Neural Network for Predicting Cervix Type and Cervical Cell Abnormalities—0
Consensus-based Distributed Quantum Kernel Learning for Speech Recognition—0
CoDiCast: Conditional Diffusion Model for Global Weather Prediction with Uncertainty QuantificationCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified