117 citations · 398 across the 51 of their papers we have counts for
7 papers · 1 filter
Unsupervised Machine Learning for Experimental Detection of Quantum-Many-Body Phase Transitions
Ron Ziv, David Wei, Antonio Rubio-Abadal +7
Quantum many-body (QMB) systems are generally computationally hard: the computing resources necessary to simulate them exactly can often exceed the existing computation resources b…
Sparsity-Driven Entanglement Detection in High-Dimensional Quantum States
Stav Lotan, Hugo Defienne, Ronen Talmon +1
The characterization of high-dimensional quantum entanglement is crucial for advanced quantum computing and quantum information algorithms. Traditional methods require extensive da…
Enhancing VICReg: Random-Walk Pairing for Improved Generalization and Better Global Semantics Capturing
Idan Simai, Ronen Talmon, Uri Shaham
In this paper, we argue that viewing VICReg-a popular self-supervised learning (SSL) method--through the lens of spectral embedding reveals a potential source of sub-optimality: it…
It Takes a Graph to Know a Graph: Rewiring for Homophily with a Reference Graph
Harel Mendelman, Haggai Maron, Ronen Talmon
Graph Neural Networks (GNNs) excel at analyzing graph-structured data but struggle on heterophilic graphs, where connected nodes often belong to different classes. While this chall…
Learning Shared Representations from Unpaired Data
Amitai Yacobi, Nir Ben-Ari, Ronen Talmon +1
Learning shared representations is a primary area of multimodal representation learning. The current approaches to achieve a shared embedding space rely heavily on paired samples f…
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…