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Hi there. I'm Philip. Would you like to look at some code that I write? In that case you're in the right place. The table below showcases some projects I have written or contributed to.

Title Description
MNIST experiment from Tensorizing neural networks (Novikov et al. 2015) This is a replication of the MNIST experiment from Tensorizing Neural Networks. The author achieves a good result on MNIST without convolutional layers. The implementation of matrix tensor train layer in this repo is not very reusable, but it’s very simple to understand!
Deep Convolutional Tensor Network This repository contains the experiments I used in my paper Deep Convolutional Tensor Network presented at 2020 NeurIPS Quantum Tensor Networks in Machine Learning Workshop. In the paper, I study a novel hybrid neural network / tensor network machine learning model.
GPT-2 Chatbot This was a student group project at Skoltech. We fine-tuned GPT-2 on dialog corpora (this was before ChatGPT), built a chatbot and launched it on Telegram.
Tensor Train Decomposition for Recurrent Neural Networks This was a student group project at Skoltech. I replicated an experiment from Tensor-Train Recurrent Neural Networks for Video Classification (while accidentally getting SOTA results). My contributed code, Report describing the experiments
Bayesian Tensorized Neural Networks with Automatic Rank Selection Another student group project where we replicated a paper but failed to obtain the same results.
libcrap A small python library with a bunch of useful code written by me and used in many of my projects.

philip-bl's Projects

blogs-by-experts icon blogs-by-experts

A curated list of blogs written by eloquent experts, organized by field.

dctn icon dctn

Deep convolutional tensor network

hpc-latencies-prediction icon hpc-latencies-prediction

A tool for predicting network latencies in HPC clusters using informationg about its topology and limited amount of ping data.

tensor_train_rnn icon tensor_train_rnn

Replace some weights matrices of linear layers of RNNs with matrices in tensor train format. Get better generalization and a lot less parameters.

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