Justin Solomon

research

∙ 05/22/2023

A Convex Optimization Framework for Regularized Geodesic Distances

We propose a general convex optimization problem for computing regulariz...

0 Michal Edelstein, et al. ∙

research

∙ 05/05/2023

Data-Free Learning of Reduced-Order Kinematics

Physical systems ranging from elastic bodies to kinematic linkages are d...

0 Nicholas Sharp, et al. ∙

research

∙ 03/25/2023

Deep Augmentation: Enhancing Self-Supervised Learning through Transformations in Higher Activation Space

We introduce Deep Augmentation, an approach to data augmentation using d...

0 Rickard Brüel Gabrielsson, et al. ∙

research

∙ 01/31/2023

Self-Consistent Velocity Matching of Probability Flows

We present a discretization-free scalable framework for solving a large ...

0 Lingxiao Li, et al. ∙

research

∙ 10/24/2022

Sampling with Mollified Interaction Energy Descent

Sampling from a target measure whose density is only known up to a norma...

0 Lingxiao Li, et al. ∙

research

∙ 10/13/2022

Outlier-Robust Group Inference via Gradient Space Clustering

Traditional machine learning models focus on achieving good performance ...

0 Yuchen Zeng, et al. ∙

research

∙ 08/02/2022

A Dataset and Benchmark for Mesh Parameterization

UV parameterization is a core task in computer graphics, with applicatio...

0 Georgia Shay, et al. ∙

research

∙ 05/18/2022

Riemannian Metric Learning via Optimal Transport

We introduce an optimal transport-based model for learning a metric tens...

0 Christopher Scarvelis, et al. ∙

research

∙ 02/05/2022

Symmetric Volume Maps

Although shape correspondence is a central problem in geometry processin...

0 S. Mazdak Abulnaga, et al. ∙

research

∙ 02/03/2022

Log-Euclidean Signatures for Intrinsic Distances Between Unaligned Datasets

The need for efficiently comparing and representing datasets with unknow...

0 Tal Shnitzer, et al. ∙

research

∙ 01/29/2022

Rewiring with Positional Encodings for Graph Neural Networks

Several recent works use positional encodings to extend the receptive fi...

0 Rickard Brüel Gabrielsson, et al. ∙

research

∙ 01/28/2022

Learning Proximal Operators to Discover Multiple Optima

Finding multiple solutions of non-convex optimization problems is a ubiq...

0 Lingxiao Li, et al. ∙

research

∙ 01/28/2022

Wassersplines for Stylized Neural Animation

Much of computer-generated animation is created by manipulating meshes w...

0 Paul Zhang, et al. ∙

research

∙ 11/17/2021

DeepCurrents: Learning Implicit Representations of Shapes with Boundaries

Recent techniques have been successful in reconstructing surfaces as lev...

6 David Palmer, et al. ∙

research

∙ 11/15/2021

Volumetric Parameterization of the Placenta to a Flattened Template

We present a volumetric mesh-based algorithm for parameterizing the plac...

0 S. Mazdak Abulnaga, et al. ∙

research

∙ 10/16/2021

Sum-of-Squares Geometry Processing

Geometry processing presents a variety of difficult numerical problems, ...

0 Zoë Marschner, et al. ∙

research

∙ 10/13/2021

Object DGCNN: 3D Object Detection using Dynamic Graphs

3D object detection often involves complicated training and testing pipe...

0 Yue Wang, et al. ∙

research

∙ 10/13/2021

DETR3D: 3D Object Detection from Multi-view Images via 3D-to-2D Queries

We introduce a framework for multi-camera 3D object detection. In contra...

9 Yue Wang, et al. ∙

research

∙ 09/13/2021

Interactive All-Hex Meshing via Cuboid Decomposition

Standard PolyCube-based hexahedral (hex) meshing methods aim to deform t...

0 Lingxiao Li, et al. ∙

research

∙ 07/30/2021

Co-Optimization of Design and Fabrication Plans for Carpentry: Supplemental Material

Past work on optimizing fabrication plans given a carpentry design can p...

0 Haisen Zhao, et al. ∙

research

∙ 07/26/2021

Co-Optimization of Design and Fabrication Plans for Carpentry

Past work on optimizing fabrication plans given a carpentry design can p...

0 Haisen Zhao, et al. ∙

research

∙ 07/12/2021

A Splitting Scheme for Flip-Free Distortion Energies

We introduce a robust optimization method for flip-free distortion energ...

0 Oded Stein, et al. ∙

research

∙ 06/28/2021

Frame Field Operators

Differential operators are widely used in geometry processing for proble...

0 David R. Palmer, et al. ∙

research

∙ 06/05/2021

k-Mixup Regularization for Deep Learning via Optimal Transport

Mixup is a popular regularization technique for training deep neural net...

0 Kristjan Greenewald, et al. ∙

research

∙ 06/03/2021

Do Neural Optimal Transport Solvers Work? A Continuous Wasserstein-2 Benchmark

Despite the recent popularity of neural network-based solvers for optima...

0 Alexander Korotin, et al. ∙

research

∙ 06/01/2021

Large-Scale Wasserstein Gradient Flows

Wasserstein gradient flows provide a powerful means of understanding and...

0 Petr Mokrov, et al. ∙

research

∙ 04/29/2021

MarioNette: Self-Supervised Sprite Learning

Visual content often contains recurring elements. Text is made up of gly...

20 Dmitriy Smirnov, et al. ∙

research

∙ 04/26/2021

HodgeNet: Learning Spectral Geometry on Triangle Meshes

Constrained by the limitations of learning toolkits engineered for other...

0 Dmitriy Smirnov, et al. ∙

research

∙ 04/12/2021

Synthesis of Frame Field-Aligned Multi-Laminar Structures

In the field of topology optimization, the homogenization approach has b...

0 Florian Cyril Stutz, et al. ∙

research

∙ 02/25/2021

Improving Approximate Optimal Transport Distances using Quantization

Optimal transport (OT) is a popular tool in machine learning to compare ...

0 Gaspard Beugnot, et al. ∙

research

∙ 02/02/2021

Continuous Wasserstein-2 Barycenter Estimation without Minimax Optimization

Wasserstein barycenters provide a geometric notion of the weighted avera...

0 Alexander Korotin, et al. ∙

research

∙ 12/14/2020

Outlier-Robust Optimal Transport

Optimal transport (OT) provides a way of measuring distances between dis...

0 Debarghya Mukherjee, et al. ∙

research

∙ 12/13/2020

k-Variance: A Clustered Notion of Variance

We introduce k-variance, a generalization of variance built on the machi...

0 Justin Solomon, et al. ∙

research

∙ 11/18/2020

Redistricting Algorithms

Why not have a computer just draw a map? This is something you hear a lo...

0 Amariah Becker, et al. ∙

research

∙ 09/24/2020

Multi-Frame to Single-Frame: Knowledge Distillation for 3D Object Detection

A common dilemma in 3D object detection for autonomous driving is that h...

0 Yue Wang, et al. ∙

research

∙ 08/28/2020

Continuous Regularized Wasserstein Barycenters

Wasserstein barycenters provide a geometrically meaningful way to aggreg...

4 Lingxiao Li, et al. ∙

research

∙ 07/20/2020

Pillar-based Object Detection for Autonomous Driving

We present a simple and flexible object detection framework optimized fo...

10 Yue Wang, et al. ∙

research

∙ 07/19/2020

Octahedral Frames for Feature-Aligned Cross-Fields

We present a method for designing smooth cross fields on surfaces that a...

0 Paul Zhang, et al. ∙

research

∙ 07/13/2020

Model Fusion with Kullback–Leibler Divergence

We propose a method to fuse posterior distributions learned from heterog...

59 Sebastian Claici, et al. ∙

research

∙ 07/02/2020

Medial Axis Isoperimetric Profiles

Recently proposed as a stable means of evaluating geometric compactness,...

0 Paul Zhang, et al. ∙

research

∙ 05/26/2020

A Computational Approach to Measuring Vote Elasticity and Competitiveness

The recent wave of attention to partisan gerrymandering has come with a ...

0 Daryl DeFord, et al. ∙

research

∙ 04/30/2020

Polygonal Building Segmentation by Frame Field Learning

While state of the art image segmentation models typically output segmen...

8 Nicolas Girard, et al. ∙

research

∙ 01/22/2020

Automatic phantom test pattern classification through transfer learning with deep neural networks

Imaging phantoms are test patterns used to measure image quality in comp...

4 Rafael B. Fricks, et al. ∙

research

∙ 12/16/2019

Incorporating Unlabeled Data into Distributionally Robust Learning

We study a robust alternative to empirical risk minimization called dist...

6 Charlie Frogner, et al. ∙

research

∙ 11/05/2019

Alleviating Label Switching with Optimal Transport

Label switching is a phenomenon arising in mixture model posterior infer...

25 Pierre Monteiller, et al. ∙

research

∙ 10/31/2019

Recombination: A family of Markov chains for redistricting

Redistricting is the problem of partitioning a set of geographical units...

0 Daryl DeFord, et al. ∙

research

∙ 10/21/2019

Geometry of Graph Partitions via Optimal Transport

We define a distance metric between partitions of a graph using machiner...

0 Tara Abrishami, et al. ∙

research

∙ 08/23/2019

Complexity and Geometry of Sampling Connected Graph Partitions

In this paper, we prove intractability results about sampling from the s...

0 Lorenzo Najt, et al. ∙

research

∙ 08/15/2019

Algebraic Representations for Volumetric Frame Fields

Field-guided parametrization methods have proven effective for quad mesh...

0 David Palmer, et al. ∙

research

∙ 06/28/2019

Deep Sketch-Based Modeling of Man-Made Shapes

Sketch-based modeling aims to model 3D geometry using a concise and easy...

0 Dmitriy Smirnov, et al. ∙

Justin Solomon

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