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 911–920 of 4891 papers

TitleStatusHype
Recurrent Memory for Online Interdomain Gaussian Processes—0
A Low-Complexity Plug-and-Play Deep Learning Model for Massive MIMO Precoding Across Sites—0
InTAR: Inter-Task Auto-Reconfigurable Accelerator Design for High Data Volume Variation in DNNsCode1
MoHAVE: Mixture of Hierarchical Audio-Visual Experts for Robust Speech Recognition—0
Fast-COS: A Fast One-Stage Object Detector Based on Reparameterized Attention Vision Transformer for Autonomous Driving—0
Mesh2SSM++: A Probabilistic Framework for Unsupervised Learning of Statistical Shape Model of Anatomies from Surface MeshesCode0
Filtered Markovian Projection: Dimensionality Reduction in Filtering for Stochastic Reaction NetworksCode0
Learning Inverse Laplacian Pyramid for Progressive Depth Completion—0
FedAPA: Server-side Gradient-Based Adaptive Personalized Aggregation for Federated Learning on Heterogeneous Data—0
Exploring Patterns Behind Sports—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified