2 citations · 3 across the 12 of their papers we have counts for
15 papers
Shared Geometry As A Rosetta Stone: Cross-Modal Alignment Without Paired Data
Dominik Schnaus, Thomas Dagès, Daniel Cremers +2
Multimodal representations enable zero-shot classification and retrieval, but aligning independently trained models usually requires large amounts of paired data. Yet, the Platonic…
Permutation-Equivariant Flow Matching for Alignment-Free Neural Weight Generation
Arkadi Piven, Yam Eitan, Guy Bar-Shalom +5
A trained neural network can be represented by a parameter vector in high dimensions. Learning distributions over these vectors enables the generation of new models across various…
Generative Shape Reconstruction with Geometry-Guided Langevin Dynamics
Linus Härenstam-Nielsen, Dmitrii Pozdeev, Thomas Dagès +2
Reconstructing complete 3D shapes from incomplete or noisy observations is a fundamentally ill-posed problem that requires balancing measurement consistency with shape plausibility…
Harnessing Data Asymmetry: Manifold Learning in the Finsler World
Thomas Dagès, Simon Weber, Daniel Cremers +1
Manifold learning is a fundamental task at the core of data analysis and visualisation. It aims to capture the simple underlying structure of complex high-dimensional data by prese…
Learning Eigenstructures of Unstructured Data Manifolds
Roy Velich, Arkadi Piven, David Bensaïd +3
We introduce a novel framework that directly learns a spectral basis for shape and manifold analysis from unstructured data, eliminating the need for traditional operator selection…
Finsler Multi-Dimensional Scaling: Manifold Learning for Asymmetric Dimensionality Reduction and Embedding
Thomas Dagès, Simon Weber, Ya-Wei Eileen Lin +5
Dimensionality reduction is a fundamental task that aims to simplify complex data by reducing its feature dimensionality while preserving essential patterns, with core applications…
Wormhole Loss for Partial Shape Matching
Amit Bracha, Thomas Dagès, Ron Kimmel
When matching parts of a surface to its whole, a fundamental question arises: Which points should be included in the matching process? The issue is intensified when using isometry…
Metric Convolutions: A Unifying Theory to Adaptive Image Convolutions
Thomas Dagès, Michael Lindenbaum, Alfred M. Bruckstein
Standard convolutions are prevalent in image processing and deep learning, but their fixed kernels limits adaptability. Several deformation strategies of the reference kernel grid…
Finsler-Laplace-Beltrami Operators with Application to Shape Analysis
Simon Weber, Thomas Dagès, Maolin Gao +1
The Laplace-Beltrami operator (LBO) emerges from studying manifolds equipped with a Riemannian metric. It is often called the Swiss army knife of geometry processing as it allows t…
On Unsupervised Partial Shape Correspondence
Amit Bracha, Thomas Dagès, Ron Kimmel
While dealing with matching shapes to their parts, we often apply a tool known as functional maps. The idea is to translate the shape matching problem into "convenient" spaces by w…
A model is worth tens of thousands of examples
Thomas Dagès, Laurent D. Cohen, Alfred M. Bruckstein
Traditional signal processing methods relying on mathematical data generation models have been cast aside in favour of deep neural networks, which require vast amounts of data. Sin…
From Compass and Ruler to Convolution and Nonlinearity: On the Surprising Difficulty of Understanding a Simple CNN Solving a Simple Geometric Estimation Task
Thomas Dagès, Michael Lindenbaum, Alfred M. Bruckstein
Neural networks are omnipresent, but remain poorly understood. Their increasing complexity and use in critical systems raises the important challenge to full interpretability. We p…
A Bound on the Edge-Flipping Distance between Triangulations (Revisiting the Proof)
Thomas Dagès, Alfred M. Bruckstein
We revisit here a fundamental result on planar triangulations, namely that the flip distance between two triangulations is upper-bounded by the number of proper intersections betwe…
Doubly Stochastic Pairwise Interactions for Agreement and Alignment
Thomas Dagès, Alfred M. Bruckstein
Random pairwise encounters often occur in large populations, or groups of mobile agents, and various types of local interactions that happen at encounters account for emergent glob…
Seeing Things in Random-Dot Videos
Thomas Dagès, Michael Lindenbaum, Alfred M. Bruckstein
Humans possess an intricate and powerful visual system in order to perceive and understand the environing world. Human perception can effortlessly detect and correctly group featur…