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 761–770 of 4891 papers

TitleStatusHype
Efficient and Information-Preserving Future Frame Prediction and BeyondCode1
EfficientPose: Scalable single-person pose estimationCode1
Self-Organized Operational Neural Networks with Generative NeuronsCode1
MiniSeg: An Extremely Minimum Network for Efficient COVID-19 SegmentationCode1
Semantic Segmentation of Underwater Imagery: Dataset and BenchmarkCode1
MUXConv: Information Multiplexing in Convolutional Neural NetworksCode1
Deep reinforcement learning for large-scale epidemic controlCode1
Spike-FlowNet: Event-based Optical Flow Estimation with Energy-Efficient Hybrid Neural NetworksCode1
Robust, Occlusion-aware Pose Estimation for Objects Grasped by Adaptive HandsCode1
Complete Dictionary Learning via _p-norm MaximizationCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified