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 1451–1460 of 4891 papers

TitleStatusHype
Multi-Grained Feature Pruning for Video-Based Human Pose Estimation—0
Machine Learned Force Fields: Fundamentals, its reach, and challenges—0
NoT: Federated Unlearning via Weight Negation—0
Language modelling techniques for analysing the impact of human genetic variation—0
Neural Configuration-Space Barriers for Manipulation Planning and Control—0
Geometry-Constrained Monocular Scale Estimation Using Semantic Segmentation for Dynamic Scenes—0
Temporal Separation with Entropy Regularization for Knowledge Distillation in Spiking Neural Networks—0
TrafficKAN-GCN: Graph Convolutional-based Kolmogorov-Arnold Network for Traffic Flow OptimizationCode0
GNNMerge: Merging of GNN Models Without Accessing Training DataCode0
LREA: Low-Rank Efficient Attention on Modeling Long-Term User Behaviors for CTR Prediction—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified