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Top Papers in Iterative optimization process

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Stress Topology Analysis for Porous Infill Optimization

The optimization of porous infill structures via local volume constraints has
become a popular approach in topology optimization. In some design settings,
however, the iterative optimization process c

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Motion-compensated MR reconstruction with dynamic motion estimation

Learning-based and unrolled motion-compensated reconstruction for cardiac MR CINE imaging

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Towards a machine learning pipeline in reduced order modelling for inverse problems: neural networks for boundary parametrization, dimensionality reduction and solution manifold approximation

In this work, we propose a model order reduction framework to deal with
inverse problems in a non-intrusive setting. Inverse problems, especially in a
partial differential equation context, require a

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Dynamic Neural Cellular Automata for Real-time and controllable Dynamic texture synthesis

DyNCA: Real-time Dynamic Texture Synthesis Using Neural Cellular Automata

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Deep Learning-Based Inverse Design for Engineering Systems: Multidisciplinary Design Optimization of Automotive Brakes

The braking performance of the brake system is a target performance that must
be considered for vehicle development. Apparent piston travel (APT) and drag
torque are the most representative factors fo

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GEOMScope: A Lensless 3D Microscope for Real-Time Object Reconstruction

GEOMScope: Large Field-of-view 3D Lensless Microscopy with Low Computational Complexity

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Possibilistic Tracking of Hybrid Soft-rigid Robots

6N-DoF Pose Tracking for Tensegrity Robots

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Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization

Gatys et al. recently introduced a neural algorithm that renders a content
image in the style of another image, achieving so-called style transfer.
However, their framework requires a slow iterative o

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A Missing Value Filling Model Based on Feature Fusion Enhanced Autoencoder

With the advent of the big data era, the data quality problem is becoming
more and more crucial. Among many factors, data with missing values is one
primary issue, and thus developing effective imputa

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Location-free Camouflage Generation Network

Location-Free Camouflage Generation Network

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Piecewise-Linear Motion Planning amidst Static, Moving, or Morphing Obstacles

We propose a novel method for planning shortest length piecewise-linear
motions through complex environments punctured with static, moving, or even
morphing obstacles. Using a moment optimization appr

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Iterative Amortized Policy Optimization

Policy networks are a central feature of deep reinforcement learning (RL)
algorithms for continuous control, enabling the estimation and sampling of
high-value actions. From the variational inference

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From inexact optimization to learning via gradient concentration

Optimization was recently shown to control the inductive bias in a learning
process, a property referred to as implicit, or iterative regularization. The
estimator obtained iteratively minimizing the

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Trainable parametric set functions for near-real-time optimization

Teaching Networks to Solve Optimization Problems

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Bayesian Optimization for Iterative Learning

Deep (reinforcement) learning systems are sensitive to hyperparameters which are notoriously expensive to tune, typically requiring running iterative processes over multiple epochs or episodes. Tradit

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Robust Gaussian Process Regression Based on Iterative Trimming

The Gaussian process (GP) regression can be severely biased when the data are
contaminated by outliers. This paper presents a new robust GP regression
algorithm that iteratively trims the most extreme

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House-GAN++: Generative Adversarial Layout Refinement Networks

This paper proposes a novel generative adversarial layout refinement network
for automated floorplan generation. Our architecture is an integration of a
graph-constrained relational GAN and a conditio

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NN Ensemble Based Active Learning for Local Geometry optimization

Machine-learning accelerated geometry optimization in molecular simulation

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Adversarial Attacks on Optimization based Planners

Trajectory planning is a key piece in the algorithmic architecture of a
robot. Trajectory planners typically use iterative optimization schemes for
generating smooth trajectories that avoid collisions

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Chance Constrained Policy Optimization for Process Control and Optimization

Chemical process optimization and control are affected by 1) plant-model mismatch, 2) process disturbances, and 3) constraints for safe operation. Reinforcement learning by policy optimization would b

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