## Project Activities

### Phase transitions in local rule learning

I will discuss the connection between the perceptron grokking scenario and the problem of learning local rules.

### Perceptron grokking

A sudden transition to zero test/generalization error in the terminal training phase in learning algorithmic tasks with deep neural networks (named grokking) has recently attracted a lot of attention. Here we discuss a simple perceptron grokking model with exact analytic solutions.

### Symmetry journal Special Issue: Symmetries in Tensor Networks and Its Applications

The journal Symmtery recently started a Special Issue focused on Symmetry in tensor networks and its applications.

### Application of deep tensor networks to MNIST and permuted MNIST

In this post I present results on applying deep tensor networks to the MNIST and permuted MNIST classification tasks.

### Deep tensor networks with the tensor-network attention layer

Tensor networks in machine learning are typically applied as linear models in an exponentially large Hilbert space. In this post, I describe a tensor-network attention layer, which enables us to build deep non-linear tensor-network models.

### Improved MPS classifier baselines on simple image datasets

In ML research, the baseline models are often not given enough attention when improving existing approaches or implementing new ones. Here we will discuss improving the vanilla matrix product state classifier on simple image datasets by enhancing the data augmentation and the training procedure.

### Popular article in Presek: A short overview of quantum machine learning

A popular science article published in Presek (2021, issue 3, category computer and information science) presenting a short overview of quantum machine learning. A translation of the article can be found below.