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Found 9 papers, 3 papers with code
Finsler Multi-Dimensional Scaling: Manifold Learning for Asymmetric Dimensionality Reduction and Embedding
Motivated by the lack of asymmetry in the Riemannian metric of the embedding space, this paper extends the MDS problem to a natural asymmetric generalisation of Riemannian manifolds called Finsler manifolds.
Wormhole Loss for Partial Shape Matching
When matching parts of a surface to its whole, a fundamental question arises: Which points should be included in the matching process?
Metric Convolutions: A Unifying Theory to Adaptive Convolutions
By returning to a metric perspective for images, now seen as two-dimensional manifolds equipped with notions of local and geodesic distances, either symmetric (Riemannian metrics) or not (Finsler metrics), we provide a unifying principle: the kernel positions are samples of unit balls of implicit metrics.
Finsler-Laplace-Beltrami Operators with Application to Shape Analysis
In experimental evaluations we demonstrate that the proposed FLBO is a valuable alternative to the traditional Riemannian-based LBO and ALBOs for spatial filtering and shape correspondence estimation.
On Unsupervised Partial Shape Correspondence
Our study of functional maps led us to a novel method that establishes direct correspondence between partial and full shapes through feature matching bypassing the need for functional map intermediate spaces.
A model is worth tens of thousands of examples
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.
From Compass and Ruler to Convolution and Nonlinearity: On the Surprising Difficulty of Understanding a Simple CNN Solving a Simple Geometric Estimation Task
Neural networks are omnipresent, but remain poorly understood.
Seeing Things in Random-Dot Videos
Humans possess an intricate and powerful visual system in order to perceive and understand the environing world.
Probabilistic Gathering Of Agents With Simple Sensors
Two types of motion are considered: when no peers are detected behind them, either the agents perform unit jumps forward, or they start to move with unit speed while continuously sensing their back half-plane, and stop whenever another agent appears there.
Multiagent Systems
