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 3291–3300 of 4891 papers

TitleStatusHype
A Batch Sequential Halving Algorithm without Performance Degradation—0
A Benchmark for Fairness-Aware Graph Learning—0
Ab Initio Nucleic Acid Folding Simulations Using a Physics-Based Atomistic Free Energy Model—0
Combining Off-White and Sparse Black Models in Multi-step Physics-based Systems Identification -- EXTENDED VERSION—0
A Bottom-up Approach for Pancreas Segmentation using Cascaded Superpixels and (Deep) Image Patch Labeling—0
Abstract Rendering: Computing All Seen in Gaussian Splat Scenes—0
Acc3D: Accelerating Single Image to 3D Diffusion Models via Edge Consistency Guided Score Distillation—0
Accelerated Deep Reinforcement Learning Based Load Shedding for Emergency Voltage Control—0
Accelerated Fully First-Order Methods for Bilevel and Minimax Optimization—0
Accelerated Sparse Recovery Under Structured Measurements—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified