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 3761–3770 of 4891 papers

TitleStatusHype
MC-CIM: Compute-in-Memory with Monte-Carlo Dropouts for Bayesian Edge Intelligence—0
Observation Error Covariance Specification in Dynamical Systems for Data assimilation using Recurrent Neural Networks—0
Offline Contextual Bandits for Wireless Network Optimization—0
Practical, Provably-Correct Interactive Learning in the Realizable Setting: The Power of True Believers—0
AI challenges for predicting the impact of mutations on protein stability—0
Statistical and Computational Efficiency for Smooth Tensor Estimation with Unknown Permutations—0
Trajectory PHD and CPHD Filters with Unknown Detection Profile—0
Dual Parameterization of Sparse Variational Gaussian ProcessesCode0
Leveraging Advantages of Interactive and Non-Interactive Models for Vector-Based Cross-Lingual Information Retrieval—0
Conformal testing: binary case with Markov alternatives—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified