16 citations · 30 across the 8 of their papers we have counts for
9 papers
MDNF: Multi-Diffusion-Nets for Neural Fields on Meshes
Avigail Cohen Rimon, Tal Shnitzer, Mirela Ben Chen
We propose a novel framework for representing neural fields on triangle meshes that is multi-resolution across both spatial and frequency domains. Inspired by the Neural Fourier Fi…
GeRA: Label-Efficient Geometrically Regularized Alignment
Dustin Klebe, Tal Shnitzer, Mikhail Yurochkin +2
Pretrained unimodal encoders incorporate rich semantic information into embedding space structures. To be similarly informative, multi-modal encoders typically require massive amou…
Large Language Model Routing with Benchmark Datasets
Tal Shnitzer, Anthony Ou, Mírian Silva +5
There is a rapidly growing number of open-source Large Language Models (LLMs) and benchmark datasets to compare them. While some models dominate these benchmarks, no single model t…
ManiFeSt: Manifold-based Feature Selection for Small Data Sets
David Cohen, Tal Shnitzer, Yuval Kluger +1
In this paper, we present a new method for few-sample supervised feature selection (FS). Our method first learns the manifold of the feature space of each class using kernels captu…
Log-Euclidean Signatures for Intrinsic Distances Between Unaligned Datasets
Tal Shnitzer, Mikhail Yurochkin, Kristjan Greenewald +1
The need for efficiently comparing and representing datasets with unknown alignment spans various fields, from model analysis and comparison in machine learning to trend discovery…
Spatiotemporal Analysis Using Riemannian Composition of Diffusion Operators
Tal Shnitzer, Hau-Tieng Wu, Ronen Talmon
Multivariate time-series have become abundant in recent years, as many data-acquisition systems record information through multiple sensors simultaneously. In this paper, we assume…
Recovering Hidden Components in Multimodal Data with Composite Diffusion Operators
Tal Shnitzer, Mirela Ben-Chen, Leonidas Guibas +2
Finding appropriate low dimensional representations of high-dimensional multi-modal data can be challenging, since each modality embodies unique deformations and interferences. In…
Diffusion Maps Kalman Filter for a Class of Systems with Gradient Flows
Tal Shnitzer, Ronen Talmon, Jean-Jacques Slotine
In this paper, we propose a non-parametric method for state estimation of high-dimensional nonlinear stochastic dynamical systems, which evolve according to gradient flows with iso…
Manifold Learning with Contracting Observers for Data-driven Time-series Analysis
Tal Shnitzer, Ronen Talmon, Jean-Jacques Slotine
Analyzing signals arising from dynamical systems typically requires many modeling assumptions and parameter estimation. In high dimensions, this modeling is particularly difficult…