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 3451–3460 of 4891 papers

TitleStatusHype
Selectively Linearized Neural Network based RoCoF-Constrained Unit Commitment in Low-Inertia Power Systems—0
A Survey on the Integration of Machine Learning with Sampling-based Motion Planning—0
Resilient Set-based State Estimation for Linear Time-Invariant Systems Using ZonotopesCode0
Edge2Vec: A High Quality Embedding for the Jigsaw Puzzle Problem—0
Evaluating Distribution System Reliability with Hyperstructures Graph Convolutional Nets—0
Zero-shot Image Captioning by Anchor-augmented Vision-Language Space Alignment—0
Soft-Landing Strategy for Alleviating the Task Discrepancy Problem in Temporal Action Localization TasksCode0
Structured Singular Value of a Repeated Complex Full-Block Uncertainty—0
Robust N-1 secure HV Grid Flexibility Estimation for TSO-DSO coordinated Congestion Management with Deep Reinforcement Learning—0
Robust DNN Surrogate Models with Uncertainty Quantification via Adversarial Training—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified