
A Splitting Scheme for FlipFree Distortion Energies
We introduce a robust optimization method for flipfree distortion energ...
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Frame Field Operators
Differential operators are widely used in geometry processing for proble...
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kMixup Regularization for Deep Learning via Optimal Transport
Mixup is a popular regularization technique for training deep neural net...
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Do Neural Optimal Transport Solvers Work? A Continuous Wasserstein2 Benchmark
Despite the recent popularity of neural networkbased solvers for optima...
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LargeScale Wasserstein Gradient Flows
Wasserstein gradient flows provide a powerful means of understanding and...
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MarioNette: SelfSupervised Sprite Learning
Visual content often contains recurring elements. Text is made up of gly...
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HodgeNet: Learning Spectral Geometry on Triangle Meshes
Constrained by the limitations of learning toolkits engineered for other...
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Synthesis of Frame FieldAligned MultiLaminar Structures
In the field of topology optimization, the homogenization approach has b...
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Improving Approximate Optimal Transport Distances using Quantization
Optimal transport (OT) is a popular tool in machine learning to compare ...
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Continuous Wasserstein2 Barycenter Estimation without Minimax Optimization
Wasserstein barycenters provide a geometric notion of the weighted avera...
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OutlierRobust Optimal Transport
Optimal transport (OT) provides a way of measuring distances between dis...
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kVariance: A Clustered Notion of Variance
We introduce kvariance, a generalization of variance built on the machi...
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Redistricting Algorithms
Why not have a computer just draw a map? This is something you hear a lo...
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MultiFrame to SingleFrame: Knowledge Distillation for 3D Object Detection
A common dilemma in 3D object detection for autonomous driving is that h...
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Continuous Regularized Wasserstein Barycenters
Wasserstein barycenters provide a geometrically meaningful way to aggreg...
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Pillarbased Object Detection for Autonomous Driving
We present a simple and flexible object detection framework optimized fo...
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Octahedral Frames for FeatureAligned CrossFields
We present a method for designing smooth cross fields on surfaces that a...
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Model Fusion with Kullback–Leibler Divergence
We propose a method to fuse posterior distributions learned from heterog...
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Medial Axis Isoperimetric Profiles
Recently proposed as a stable means of evaluating geometric compactness,...
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A Computational Approach to Measuring Vote Elasticity and Competitiveness
The recent wave of attention to partisan gerrymandering has come with a ...
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Polygonal Building Segmentation by Frame Field Learning
While state of the art image segmentation models typically output segmen...
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Automatic phantom test pattern classification through transfer learning with deep neural networks
Imaging phantoms are test patterns used to measure image quality in comp...
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Incorporating Unlabeled Data into Distributionally Robust Learning
We study a robust alternative to empirical risk minimization called dist...
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Alleviating Label Switching with Optimal Transport
Label switching is a phenomenon arising in mixture model posterior infer...
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Recombination: A family of Markov chains for redistricting
Redistricting is the problem of partitioning a set of geographical units...
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Geometry of Graph Partitions via Optimal Transport
We define a distance metric between partitions of a graph using machiner...
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Complexity and Geometry of Sampling Connected Graph Partitions
In this paper, we prove intractability results about sampling from the s...
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Algebraic Representations for Volumetric Frame Fields
Fieldguided parametrization methods have proven effective for quad mesh...
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Deep SketchBased Modeling of ManMade Shapes
Sketchbased modeling aims to model 3D geometry using a concise and easy...
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Hierarchical Optimal Transport for Document Representation
The ability to measure similarity between documents enables intelligent ...
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Audio Transport: A Generalized Portamento via Optimal Transport
This paper proposes a new method to interpolate between two audio signal...
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Learning Embeddings into Entropic Wasserstein Spaces
Euclidean embeddings of data are fundamentally limited in their ability ...
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Deep Parametric Shape Predictions using Distance Fields
Many tasks in graphics and vision demand machinery for converting shapes...
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Placental Flattening via Volumetric Parameterization
We present a volumetric meshbased algorithm for flattening the placenta...
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Functional Maps Representation on Product Manifolds
We consider the tasks of representing, analyzing and manipulating maps b...
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Total Variation Isoperimetric Profiles
Applications in political redistricting demand quantitative measures of ...
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Dynamical Optimal Transport on Discrete Surfaces
We propose a technique for interpolating between probability distributio...
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Wasserstein Coresets for Lipschitz Costs
Sparsification is becoming more and more relevant with the proliferation...
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Gerrymandering and Compactness: Implementation Flexibility and Abuse
The shape of an electoral district may suggest whether it was drawn with...
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Stochastic Wasserstein Barycenters
We present a stochastic algorithm to compute the barycenter of a set of ...
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Optimal Transport on Discrete Domains
Inspired by the matching of supply to demand in logistical problems, the...
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Reversible Harmonic Maps between Discrete Surfaces
Information transfer between triangle meshes is of great importance in c...
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Vectorization of Line Drawings via PolyVector Fields
Image tracing is a foundational component of the workflow in graphic des...
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Steklov Spectral Geometry for Extrinsic Shape Analysis
We propose using the DirichlettoNeumann operator as an extrinsic alter...
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Steklov Geometry Processing: An Extrinsic Approach to Spectral Shape Analysis
We propose Steklov geometry processing, an extrinsic approach to spectra...
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Parallel Streaming Wasserstein Barycenters
Efficiently aggregating data from different sources is a challenging pro...
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QuadraticallyRegularized Optimal Transport on Graphs
Optimal transportation provides a means of lifting distances between poi...
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Quantum Optimal Transport for Tensor Field Processing
This article introduces a new notion of optimal transport (OT) between t...
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ContinuousFlow Graph Transportation Distances
Optimal transportation distances are valuable for comparing and analyzin...
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A General Framework for Bilateral and Mean Shift Filtering
We present a generalization of the bilateral filter that can be applied ...
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Justin Solomon
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Assistant Professor at Massachusetts Institute of Technology (MIT) since 2016, Postdoctoral Fellow, Program in Applied and Computational Mathematics at Princeton University 20152016, PhD student and Teaching Fellow, Department of Computer Science at Stanford University from 20102015, Research Assistant at Pixar Animation Studios from 20072012.