Autoinverse: Uncertainty Aware Inversion of Neural Networks
- URL: http://arxiv.org/abs/2208.13780v1
- Date: Mon, 29 Aug 2022 12:09:32 GMT
- Title: Autoinverse: Uncertainty Aware Inversion of Neural Networks
- Authors: Navid Ansari, Hans-Peter Seidel, Nima Vahidi Ferdowsi, Vahid Babaei
- Abstract summary: We propose Autoinverse, a highly automated approach for inverting neural network surrogates.
Our main insight is to seek inverse solutions in the vicinity of reliable data which have been sampled form the forward process.
We verify our proposed method through addressing a set of real-world problems in control, fabrication, and design.
- Score: 22.759930986110625
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Neural networks are powerful surrogates for numerous forward processes. The
inversion of such surrogates is extremely valuable in science and engineering.
The most important property of a successful neural inverse method is the
performance of its solutions when deployed in the real world, i.e., on the
native forward process (and not only the learned surrogate). We propose
Autoinverse, a highly automated approach for inverting neural network
surrogates. Our main insight is to seek inverse solutions in the vicinity of
reliable data which have been sampled form the forward process and used for
training the surrogate model. Autoinverse finds such solutions by taking into
account the predictive uncertainty of the surrogate and minimizing it during
the inversion. Apart from high accuracy, Autoinverse enforces the feasibility
of solutions, comes with embedded regularization, and is initialization free.
We verify our proposed method through addressing a set of real-world problems
in control, fabrication, and design.
Related papers
- REBEL: Reward Regularization-Based Approach for Robotic Reinforcement Learning from Human Feedback [61.54791065013767]
A misalignment between the reward function and human preferences can lead to catastrophic outcomes in the real world.
Recent methods aim to mitigate misalignment by learning reward functions from human preferences.
We propose a novel concept of reward regularization within the robotic RLHF framework.
arXiv Detail & Related papers (2023-12-22T04:56:37Z) - End-to-End Meta-Bayesian Optimisation with Transformer Neural Processes [52.818579746354665]
This paper proposes the first end-to-end differentiable meta-BO framework that generalises neural processes to learn acquisition functions via transformer architectures.
We enable this end-to-end framework with reinforcement learning (RL) to tackle the lack of labelled acquisition data.
arXiv Detail & Related papers (2023-05-25T10:58:46Z) - Enhancing Inverse Problem Solutions with Accurate Surrogate Simulators
and Promising Candidates [0.7499722271664147]
The impact of surrogate simulators' accuracy on the solutions in the neural adjoint (NA) method remains uncertain.
We develop an extension of the NA method, named Neural Lagrangian (NeuLag) method, capable of efficiently optimizing a sufficient number of solution candidates.
arXiv Detail & Related papers (2023-04-26T23:05:57Z) - Accelerating Inverse Learning via Intelligent Localization with
Exploratory Sampling [1.5976506570992293]
solving inverse problems is a longstanding challenge in materials and drug discovery.
Deep generative models are recently proposed to solve inverse problems.
We propose a novel approach (called iPage) to accelerate the inverse learning process.
arXiv Detail & Related papers (2022-12-02T08:00:04Z) - Deep Preconditioners and their application to seismic wavefield
processing [0.0]
Sparsity-promoting inversion, coupled with fixed-basis sparsifying transforms, represent the go-to approach for many processing tasks.
We propose to train an AutoEncoder network to learn a direct mapping between the input seismic data and a representative latent manifold.
The trained decoder is subsequently used as a nonlinear preconditioner for the physics-driven inverse problem at hand.
arXiv Detail & Related papers (2022-07-20T14:25:32Z) - Towards Explainable Metaheuristic: Mining Surrogate Fitness Models for
Importance of Variables [69.02115180674885]
We use four benchmark problems to train a surrogate model and investigate the learning of the search space by the surrogate model.
We show that the surrogate model picks out key characteristics of the problem as it is trained on population data from each generation.
arXiv Detail & Related papers (2022-05-31T09:16:18Z) - Verifying Inverse Model Neural Networks [39.4062479625023]
Inverse problems exist in a wide variety of physical domains from aerospace engineering to medical imaging.
We introduce a method for verifying the correctness of inverse model neural networks.
arXiv Detail & Related papers (2022-02-04T23:13:22Z) - Robust lEarned Shrinkage-Thresholding (REST): Robust unrolling for
sparse recover [87.28082715343896]
We consider deep neural networks for solving inverse problems that are robust to forward model mis-specifications.
We design a new robust deep neural network architecture by applying algorithm unfolding techniques to a robust version of the underlying recovery problem.
The proposed REST network is shown to outperform state-of-the-art model-based and data-driven algorithms in both compressive sensing and radar imaging problems.
arXiv Detail & Related papers (2021-10-20T06:15:45Z) - Offline Model-Based Optimization via Normalized Maximum Likelihood
Estimation [101.22379613810881]
We consider data-driven optimization problems where one must maximize a function given only queries at a fixed set of points.
This problem setting emerges in many domains where function evaluation is a complex and expensive process.
We propose a tractable approximation that allows us to scale our method to high-capacity neural network models.
arXiv Detail & Related papers (2021-02-16T06:04:27Z) - Deep Feedback Inverse Problem Solver [141.26041463617963]
We present an efficient, effective, and generic approach towards solving inverse problems.
We leverage the feedback signal provided by the forward process and learn an iterative update model.
Our approach does not have any restrictions on the forward process; it does not require any prior knowledge either.
arXiv Detail & Related papers (2021-01-19T16:49:06Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.