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 2231–2240 of 4891 papers

TitleStatusHype
PNR: Physics-informed Neural Representation for high-resolution LFM reconstruction—0
Grid Evolution for Doubly Fractional Channel Estimation in OTFS Systems—0
Unifying Dimensions: A Linear Adaptive Approach to Lightweight Image Super-ResolutionCode0
A parametric framework for kernel-based dynamic mode decomposition using deep learningCode0
Most Influential Subset Selection: Challenges, Promises, and BeyondCode0
CNN Mixture-of-Depths—0
Exploring Brain Network Organization in Alzheimer Disease and Frontotemporal Dementia: A Crossplot Transition Entropy Approach—0
Classification of Gleason Grading in Prostate Cancer Histopathology Images Using Deep Learning Techniques: YOLO, Vision Transformers, and Vision MambaCode0
3DDX: Bone Surface Reconstruction from a Single Standard-Geometry Radiograph via Dual-Face Depth EstimationCode0
Learning Representation for Multitask learning through Self Supervised Auxiliary learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified