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

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Showing 1371–1380 of 4891 papers

TitleStatusHype
DeepHGCN: Toward Deeper Hyperbolic Graph Convolutional NetworksCode0
Learning Structured Representations by Embedding Class Hierarchy with Fast Optimal TransportCode0
Accelerated Alternating Projections for Robust Principal Component AnalysisCode0
DPC-Net: Deep Pose Correction for Visual LocalizationCode0
FORTRESS: Function-composition Optimized Real-Time Resilient Structural Segmentation via Kolmogorov-Arnold Enhanced Spatial Attention NetworksCode0
FinNet: Solving Time-Independent Differential Equations with Finite Difference Neural NetworkCode0
First Exit Time Analysis of Stochastic Gradient Descent Under Heavy-Tailed Gradient NoiseCode0
Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse HypergraphsCode0
DeepFDR: A Deep Learning-based False Discovery Rate Control Method for Neuroimaging DataCode0
Finding Influential Training Samples for Gradient Boosted Decision TreesCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified