2.9k citations · 10.7k across the 136 of their papers we have counts for
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Physics-informed PointNet: On how many irregular geometries can it solve an inverse problem simultaneously? Application to linear elasticity
Ali Kashefi, Leonidas J. Guibas, Tapan Mukerji
Regular physics-informed neural networks (PINNs) predict the solution of partial differential equations using sparse labeled data but only over a single domain. On the other hand,…
Intrinsic Dimension, Persistent Homology and Generalization in Neural Networks
Tolga Birdal, Aaron Lou, Leonidas Guibas +1
Disobeying the classical wisdom of statistical learning theory, modern deep neural networks generalize well even though they typically contain millions of parameters. Recently, it…
SketchGen: Generating Constrained CAD Sketches
Wamiq Reyaz Para, Shariq Farooq Bhat, Paul Guerrero +4
Computer-aided design (CAD) is the most widely used modeling approach for technical design. The typical starting point in these designs is 2D sketches which can later be extruded a…
A Point-Cloud Deep Learning Framework for Prediction of Fluid Flow Fields on Irregular Geometries
Ali Kashefi, Davis Rempe, Leonidas J. Guibas
We present a novel deep learning framework for flow field predictions in irregular domains when the solution is a function of the geometry of either the domain or objects inside th…
Representation Learning Through Latent Canonicalizations
Or Litany, Ari Morcos, Srinath Sridhar +2
We seek to learn a representation on a large annotated data source that generalizes to a target domain using limited new supervision. Many prior approaches to this problem have foc…
Side-Tuning: A Baseline for Network Adaptation via Additive Side Networks
Jeffrey O Zhang, Alexander Sax, Amir Zamir +2
When training a neural network for a desired task, one may prefer to adapt a pre-trained network rather than starting from randomly initialized weights. Adaptation can be useful in…
Quaternion Equivariant Capsule Networks for 3D Point Clouds
Yongheng Zhao, Tolga Birdal, Jan Eric Lenssen +3
We present a 3D capsule module for processing point clouds that is equivariant to 3D rotations and translations, as well as invariant to permutations of the input points. The opera…
A Topology Layer for Machine Learning
Rickard Brüel-Gabrielsson, Bradley J. Nelson, Anjan Dwaraknath +3
Topology applied to real world data using persistent homology has started to find applications within machine learning, including deep learning. We present a differentiable topolog…
PeerNets: Exploiting Peer Wisdom Against Adversarial Attacks
Jan Svoboda, Jonathan Masci, Federico Monti +2
Deep learning systems have become ubiquitous in many aspects of our lives. Unfortunately, it has been shown that such systems are vulnerable to adversarial attacks, making them pro…
Scalable Semidefinite Relaxation for Maximum A Posterior Estimation
Qixing Huang, Yuxin Chen, Leonidas Guibas
Maximum a posteriori (MAP) inference over discrete Markov random fields is a fundamental task spanning a wide spectrum of real-world applications, which is known to be NP-hard for…
Near-Optimal Joint Object Matching via Convex Relaxation
Yuxin Chen, Leonidas J. Guibas, Qi-Xing Huang
Joint matching over a collection of objects aims at aggregating information from a large collection of similar instances (e.g. images, graphs, shapes) to improve maps between pairs…