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 551–560 of 4891 papers

TitleStatusHype
A Non-autoregressive Multi-Horizon Flight Trajectory Prediction Framework with Gray Code RepresentationCode1
Posterior Sampling for Deep Reinforcement LearningCode1
Discriminative Co-Saliency and Background Mining Transformer for Co-Salient Object DetectionCode1
Two Birds, One Stone: A Unified Framework for Joint Learning of Image and Video Style TransfersCode1
Learning in latent spaces improves the predictive accuracy of deep neural operatorsCode1
GPr-Net: Geometric Prototypical Network for Point Cloud Few-Shot LearningCode1
DynamicDet: A Unified Dynamic Architecture for Object DetectionCode1
InterFormer: Real-time Interactive Image SegmentationCode1
Generative Multiplane Neural Radiance for 3D-Aware Image GenerationCode1
Not All Features Matter: Enhancing Few-shot CLIP with Adaptive Prior RefinementCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified