Inference Optimization
9 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.
Representing Edge Flows on Graphs via Sparse Cell Complexes
In this paper, we generalize this approach to cellular complexes and introduce the flow representation learning problem, i. e., the problem of augmenting the observed graph by a set of cells, such that the eigenvectors of the associated Hodge Laplacian provide a sparse, interpretable representation of the observed edge flows on the graph.
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.
Adaptive Deep Neural Network Inference Optimization with EENet
git-disl/eenet
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Instead of having every sample go through all DNN layers during prediction, EENet learns an early exit scheduler, which can intelligently terminate the inference earlier for certain predictions, which the model has high confidence of early exit.
Painterly Image Harmonization using Diffusion Model
bcmi/phdiffusion-painterly-image-harmonization
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Painterly image harmonization aims to insert photographic objects into paintings and obtain artistically coherent composite images.
