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 2821–2830 of 4891 papers

TitleStatusHype
Tumor-Centered Patching for Enhanced Medical Image Segmentation—0
Accel-GCN: High-Performance GPU Accelerator Design for Graph Convolution NetworksCode1
TurboViT: Generating Fast Vision Transformers via Generative Architecture Search—0
Split Learning for Distributed Collaborative Training of Deep Learning Models in Health Informatics—0
Domain Reduction Strategy for Non Line of Sight ImagingCode0
Efficient Real-time Path Planning with Self-evolving Particle Swarm Optimization in Dynamic ScenariosCode1
Learning Computational Efficient Bots with Costly Features—0
Artificial-Spiking Hierarchical Networks for Vision-Language Representation Learning—0
Online Transition-Based Feature Generation for Anomaly Detection in Concurrent Data Streams—0
Spike-and-slab shrinkage priors for structurally sparse Bayesian neural networks—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified