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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 301–310 of 4891 papers

TitleStatusHype
AdaRank: Adaptive Rank Pruning for Enhanced Model MergingCode1
EViT: An Eagle Vision Transformer with Bi-Fovea Self-AttentionCode1
Adaptive wavelet distillation from neural networks through interpretationsCode1
Adaptive Transformers for Learning Multimodal RepresentationsCode1
Exploiting Deblurring Networks for Radiance FieldsCode1
Event-based Video Reconstruction via Potential-assisted Spiking Neural NetworkCode1
Towards Reliable Medical Image Segmentation by utilizing Evidential Calibrated UncertaintyCode1
Bidirectional Recurrence for Cardiac Motion Tracking with Gaussian Process Latent CodingCode1
Checkerboard Context Model for Efficient Learned Image CompressionCode1
Evidential Deep Learning: Enhancing Predictive Uncertainty Estimation for Earth System Science ApplicationsCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified