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 4031–4040 of 4891 papers

TitleStatusHype
Distance Encoding: Design Provably More Powerful Neural Networks for Graph Representation LearningCode1
Is the space complexity of planted clique recovery the same as that of detection?—0
Locally induced Gaussian processes for large-scale simulation experiments—0
ETC-NLG: End-to-end Topic-Conditioned Natural Language GenerationCode0
Joint Design of RF and gradient waveforms via auto-differentiation for 3D tailored excitation in MRICode1
Neural Bridge Sampling for Evaluating Safety-Critical Autonomous Systems—0
An Unsupervised Approach to Ultrasound Elastography with End-to-end Strain Regularisation—0
Fuzzy SLIC: Fuzzy Simple Linear Iterative Clustering—0
Adversarial Imitation Learning via Random Search—0
Doubly Stochastic Variational Inference for Neural Processes with Hierarchical Latent Variables—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified