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 1411–1420 of 4891 papers

TitleStatusHype
FGP: Feature-Gradient-Prune for Efficient Convolutional Layer PruningCode0
Data-to-Model Distillation: Data-Efficient Learning FrameworkCode0
S3TU-Net: Structured Convolution and Superpixel Transformer for Lung Nodule Segmentation—0
SynCoTrain: A Dual Classifier PU-learning Framework for Synthesizability Prediction—0
Robust and Constrained Estimation of State-Space Models: A Majorization-Minimization Approach—0
Integrating and Comparing Radiality Constraints for Optimized Distribution System Reconfiguration—0
MoE-Lightning: High-Throughput MoE Inference on Memory-constrained GPUs—0
SEFD: Semantic-Enhanced Framework for Detecting LLM-Generated TextCode0
TSFormer: A Robust Framework for Efficient UHD Image Restoration—0
EROAM: Event-based Camera Rotational Odometry and Mapping in Real-time—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified