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 591–600 of 4891 papers

TitleStatusHype
Accelerated and interpretable oblique random survival forestsCode1
Augmented Lagrangian Adversarial AttacksCode1
Attention U-Net: Learning Where to Look for the PancreasCode1
Energy-based physics-informed neural network for frictionless contact problems under large deformationCode1
AIM 2024 Challenge on UHD Blind Photo Quality AssessmentCode1
Deep Generalization of Structured Low-Rank Algorithms (Deep-SLR)Code1
Deep Learning for Hate Speech Detection: A Comparative StudyCode1
SE(3) Equivariant Graph Neural Networks with Complete Local FramesCode1
DeepZero: Scaling up Zeroth-Order Optimization for Deep Model TrainingCode1
DCT-SNN: Using DCT to Distribute Spatial Information over Time for Learning Low-Latency Spiking Neural NetworksCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified