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Found 21 papers, 6 papers with code
Fairness-aware organ exchange and kidney paired donation
The kidney paired donation (KPD) program provides an innovative solution to overcome incompatibility challenges in kidney transplants by matching incompatible donor-patient pairs and facilitating kidney exchanges.
Machine learning for modelling unstructured grid data in computational physics: a review
This review is intended as a guidebook for computational scientists seeking to apply ML approaches to unstructured grid data in their domains, as well as for ML researchers looking to address challenges in computational physics.
BAG: Body-Aligned 3D Wearable Asset Generation
Finally, by recovering the similarity transformation using multiview silhouette supervision and addressing asset-body penetration with physics simulators, the 3D asset can be accurately fitted onto the target human body.
PhyCAGE: Physically Plausible Compositional 3D Asset Generation from a Single Image
We present PhyCAGE, the first approach for physically plausible compositional 3D asset generation from a single image.
Towards Universal Mesh Movement Networks
In this paper, we introduce the Universal Mesh Movement Network (UM2N), which -- once trained -- can be applied in a non-intrusive, zero-shot manner to move meshes with different size distributions and structures, for solvers applicable to different PDE types and boundary geometries.
End-to-end Wind Turbine Wake Modelling with Deep Graph Representation Learning
Wind turbine wake modelling is of crucial importance to accurate resource assessment, to layout optimisation, and to the operational control of wind farms.
E2N: Error Estimation Networks for Goal-Oriented Mesh Adaptation
We demonstrate that this approach is able to obtain the same accuracy with a reduced computational cost, for adaptive mesh test cases related to flow around tidal turbines, which interact via their downstream wakes, and where the overall power output of the farm is taken as the QoI.
Learning to Estimate and Refine Fluid Motion with Physical Dynamics
General optical flow methods are typically designed for rigid body motion, and thus struggle if applied to fluid motion estimation directly.
Complex Locomotion Skill Learning via Differentiable Physics
Differentiable physics enables efficient gradient-based optimizations of neural network (NN) controllers.
M2N: Mesh Movement Networks for PDE Solvers
However, mesh movement methods, such as the Monge-Ampere method, require the solution of auxiliary equations, which can be extremely expensive especially when the mesh is adapted frequently.
SoftCollage: A Differentiable Probabilistic Tree Generator for Image Collage
While several recent works exploit tree-based algorithm to preserve image content better, all of them resort to hand-crafted adjustment rules to optimize the collage tree structure, leading to the failure of fully exploring the structure space of collage tree.
Aesthetic Photo Collage with Deep Reinforcement Learning
To overcome the lack of training data, we pretrain our deep aesthetic network on a large scale image aesthetic dataset (CPC) for general aesthetic feature extraction and propose an attention fusion module for structural collage feature representation.
Scalable Projection-Free Optimization
As a projection-free algorithm, Frank-Wolfe (FW) method, also known as conditional gradient, has recently received considerable attention in the machine learning community.
Unsupervised Learning of Particle Image Velocimetry
Recently, the development of deep learning based methods has inspired new approaches to tackle the PIV problem.
More Data Can Expand the Generalization Gap Between Adversarially Robust and Standard Models
Despite remarkable success in practice, modern machine learning models have been found to be susceptible to adversarial attacks that make human-imperceptible perturbations to the data, but result in serious and potentially dangerous prediction errors.
Online Continuous Submodular Maximization: From Full-Information to Bandit Feedback
In this paper, we propose three online algorithms for submodular maximisation.
One Sample Stochastic Frank-Wolfe
One of the beauties of the projected gradient descent method lies in its rather simple mechanism and yet stable behavior with inexact, stochastic gradients, which has led to its wide-spread use in many machine learning applications.
Self-Paced Video Data Augmentation with Dynamic Images Generated by Generative Adversarial Networks
The dynamic image compresses the motion information of video into a still image, removing the interference factors such as the background.
Quantized Frank-Wolfe: Faster Optimization, Lower Communication, and Projection Free
How can we efficiently mitigate the overhead of gradient communications in distributed optimization?
Black Box Submodular Maximization: Discrete and Continuous Settings
In this paper, we consider the problem of black box continuous submodular maximization where we only have access to the function values and no information about the derivatives is provided.
Projection-Free Bandit Convex Optimization
In this paper, we propose the first computationally efficient projection-free algorithm for bandit convex optimization (BCO).
