Inference Optimization
6 papers with code • 0 benchmarks • 0 datasets
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Most implemented papers
Input Convex Neural Networks
We show that many existing neural network architectures can be made input-convex with a minor modification, and develop specialized optimization algorithms tailored to this setting.
Iterative Amortized Inference
joelouismarino/iterative_inference
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ICML 2018
The failure of these models to reach fully optimized approximate posterior estimates results in an amortization gap.
A General Method for Amortizing Variational Filtering
joelouismarino/amortized-variational-filtering
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NeurIPS 2018
We introduce the variational filtering EM algorithm, a simple, general-purpose method for performing variational inference in dynamical latent variable models using information from only past and present variables, i. e. filtering.
Easy and Efficient Transformer : Scalable Inference Solution For large NLP model
NetEase-FuXi/EET
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To fill such a gap, we introduce a scalable inference solution: Easy and Efficient Transformer (EET), including a series of transformer inference optimization at the algorithm and implementation levels.
A Novel 1D State Space for Efficient Music Rhythmic Analysis
Inferring music time structures has a broad range of applications in music production, processing and analysis.
ADJUST: A Dictionary-Based Joint Reconstruction and Unmixing Method for Spectral Tomography
However, these methods inherently suffer from the ill-posedness of the joint reconstruction problem.
