Learning by coincidence: Siamese networks and common variable learning

By Uri Shaham, Roy R. Lederman. 2018

38 citations recorded by the source corpus.

Abstract We consider the statistical problem of learning a common source of variability in data which are synchronously captured by multiple sensors, and demonstrate that Siamese neural networks can be naturally applied to this problem. This approach is useful in particular in exploratory, data-driven applications, where neither a model nor label information is available. In recent years, many researchers have successfully applied Siamese neural networks to obtain an embedding of data which corresponds to a “semantic similarity”. We present an interpretation of this “semantic similarity” as learning of equivalence classes. We demonstrate the ability of Siamese networks to learn common variability in a range of experiments on synthetic and real-world data, and demonstrate the potential of Siamese networks to provide new leads for data-driven research through unsupervised learning in cancer data.

Subfields: Artificial Intelligence, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Yuval Kluger
2. Ronen Basri
3. Uri Shaham
4. Boaz Nadler
5. Kelly P. Stanton
6. Jacob Steinhardt

In Signal Processing

1. Ronen Basri
2. Boaz Nadler
3. Yuval Kluger

Citations in the corpus

• SpectralNet: Spectral Clustering using Deep Neural Networks (2018, methodology): “Siamese nets (Hadsell et al., 2006; Shaham & Lederman, 2018) are trained to learn affinity relations between data points; we empirically found that the unsupervised application of a Siamese net to determine the distances often improves the quality of the clustering.”