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 4526–4550 of 4891 papers

TitleStatusHype
Fast Rigid Motion Segmentation via Incrementally-Complex Local Models—0
Fast Saturating Gate for Learning Long Time Scales with Recurrent Neural Networks—0
Fast Screening Algorithm for Rotation and Scale Invariant Template Matching—0
Fast Simulation of Particulate Suspensions Enabled by Graph Neural Network—0
Fast Stochastic MPC using Affine Disturbance Feedback Gains Learned Offline—0
FastSurvival: Hidden Computational Blessings in Training Cox Proportional Hazards Models—0
Fast Task-Aware Architecture Inference—0
Fast TILs -- A Pipeline for Efficient TILs Estimation in Non-Small Cell Lung Cancer—0
Fast Uncertainty Quantification of Spent Nuclear Fuel with Neural Networks—0
Fast Word Error Rate Estimation Using Self-Supervised Representations for Speech and Text—0
Fault Detection and Human Intervention in Vehicle Platooning: A Multi-Model Framework—0
Feature Aggregating Network with Inter-Frame Interaction for Efficient Video Super-Resolution—0
Feature Analysis for Machine Learning-based IoT Intrusion Detection—0
Feature Extraction of Hyperspectral Images With Image Fusion and Recursive Filtering—0
Feature Network Methods in Machine Learning and Applications—0
Feature Normalization Prevents Collapse of Non-contrastive Learning Dynamics—0
Feature Selection via GANs (GANFS): Enhancing Machine Learning Models for DDoS Mitigation—0
Feature-Specific Coefficients of Determination in Tree Ensembles—0
Feature subset selection for kernel SVM classification via mixed-integer optimization—0
FedAPA: Server-side Gradient-Based Adaptive Personalized Aggregation for Federated Learning on Heterogeneous Data—0
FedBlockHealth: A Synergistic Approach to Privacy and Security in IoT-Enabled Healthcare through Federated Learning and Blockchain—0
FedCanon: Non-Convex Composite Federated Learning with Efficient Proximal Operation on Heterogeneous Data—0
Federated Fine-Tuning of LLMs on the Very Edge: The Good, the Bad, the Ugly—0
Federated Instruction Tuning of LLMs with Domain Coverage Augmentation—0
Federated K-Means Clustering via Dual Decomposition-based Distributed Optimization—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified