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 771–780 of 4891 papers

TitleStatusHype
Transformer Hawkes ProcessCode1
πVAE: a stochastic process prior for Bayesian deep learning with MCMCCode1
ACEnet: Anatomical Context-Encoding Network for Neuroanatomy SegmentationCode1
SparseIDS: Learning Packet Sampling with Reinforcement LearningCode1
3-D Short-Range Imaging With Irregular MIMO Arrays Using NUFFT-Based Range Migration AlgorithmCode1
AMR Similarity Metrics from PrinciplesCode1
stream-learn -- open-source Python library for difficult data stream batch analysisCode1
Fast Sequence-Based Embedding with Diffusion GraphsCode1
The gap between theory and practice in function approximation with deep neural networksCode1
Deep Generalization of Structured Low-Rank Algorithms (Deep-SLR)Code1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified