
## tntorch: Tensor Network Learning with PyTorch
Mikhail Usvyatsov, Rafael Ballester-Ripoll, Konrad Schindler
Keywords: 
JMLR/2022/Proceedings/211197 - tntorch: Tensor Network Learning with PyTorch.pdf
Project URL: https://github.com/rballester/tntorch

### Implementation
_Given the documentation given by the authors on the method, how much time investment would it be to re-implement the method from scratch?_

[1]

The authors present a library, provide the link (https://github.com/rballester/tntorch) and various examples/tutorials. Installations notes given. Seperate documentation website for the code.

### Data
_Given the data description in the documentation, how much effort take to either: Find the same dataset the authors used, or similar datasets and defend the comparability, or acquire one from scratch?_

[1]

(1/1)

Data is randomly initialised tensors of varying sizes. 

### Configuration 
_Given the (hyper)parameters, including semantic parameters, of the method: How much effort would it take to acquire the algorithm configurations used for their results, and compare against their budgetary constraints?_

[1]

N/A

### Experimental Procedure
_Given the experimental set-up of the work, how difficult is it to set up a new experiment, similar to those presented in the original work, with the same procedure?_

[1]

The authors measure runtime of operations averaged over 10 runs. 

### Expertise
_How much effort would it take to acquire the expertise required to reproduce the work independently relying on the available documentation?_

[3]

Requries expertise on tensor operation and computational runtime (both CPU and GPU) measurements.
