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 2676–2700 of 4891 papers

TitleStatusHype
The Significance of Machine Learning in Clinical Disease Diagnosis: A Review—0
Crowd-Certain: Label Aggregation in Crowdsourced and Ensemble Learning Classification—0
Learning Continuous Network Emerging Dynamics from Scarce Observations via Data-Adaptive Stochastic ProcessesCode1
Robust and Actively Secure Serverless Collaborative Learning—0
Structured Multi-Track Accompaniment Arrangement via Style Prior ModellingCode1
Online Two-stage Thermal History Prediction Method for Metal Additive Manufacturing of Thin Walls—0
Numerical Derivative-based Flexible Integration Algorithm for Power Electronic Systems Simulation Considering Nonlinear Components—0
Variator: Accelerating Pre-trained Models with Plug-and-Play Compression ModulesCode0
Nested Control Co-design of a Spar Buoy Horizontal-axis Floating Offshore Wind Turbine—0
qPOTS: Efficient batch multiobjective Bayesian optimization via Pareto optimal Thompson samplingCode1
ADMM Algorithms for Residual Network Training: Convergence Analysis and Parallel Implementation—0
Adaptive End-to-End Metric Learning for Zero-Shot Cross-Domain Slot FillingCode0
Random Exploration in Bayesian Optimization: Order-Optimal Regret and Computational Efficiency—0
Studying K-FAC Heuristics by Viewing Adam through a Second-Order LensCode0
Efficient Cross-Task Prompt Tuning for Few-Shot Conversational Emotion Recognition—0
Delayed Memory Unit: Modelling Temporal Dependency Through Delay GateCode0
Scrap Your Schedules with PopDescent—0
EDGE++: Improved Training and Sampling of EDGE—0
Accelerated sparse Kernel Spectral Clustering for large scale data clustering problemsCode0
Online energy management system for a fuel cell/battery hybrid system with multiple fuel cell stacks—0
DeepFDR: A Deep Learning-based False Discovery Rate Control Method for Neuroimaging DataCode0
DeepFracture: A Generative Approach for Predicting Brittle Fractures with Neural Discrete Representation Learning—0
Streamlining Brain Tumor Classification with Custom Transfer Learning in MRI Images—0
Efficient Long-Range Transformers: You Need to Attend More, but Not Necessarily at Every Layer—0
Jorge: Approximate Preconditioning for GPU-efficient Second-order Optimization—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified