Machine learning and natural language processing

8 LSTM. RNN/LSTM case studies. ILP

LSTM

Ray Mooney deep learning

IEEE Trans. on NN and Learn. Sys. 2019 A Survey of the Usages of Deep Learning for Natural Language Processing

Understanding LSTM

How to learn long term trends with LSTM

RNN/LSTM case studies


Michal Hala

Code Failure Prediction and Pattern Extraction using LSTM Networks

Tomáš Houfek

Predicting life expectancy with a long short-term memory recurrent neural network using electronic medical records

Andrej Betík

Two new papers discuss how Alexa recognizes sounds


Dominik Tuchyňa

Predicting In-game Actions from Interviews of NBA Players

Radoslav Sabol

Abstractive text summarization using LSTM-CNN based deep learning


ILP

Intro to ILP

Poster session. Preliminary program

Neural Code Search: How Facebook Uses Neural Networks to Help Developers Search for Code Snippets

Andrej Betík

(ori)

XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalization

Michal Hala

(ori)

Hierarchical Attention Network

Tomáš Houfek

Hierarchical Attention Networks for Document Classification

Deep learning for question answering in Czech

Radoslav Sabol


Transformers

Dominik Tuchyňa


QUESTIONS AND TASKS:

  • Why we need LSTM. Describe a task where LSTM areuseful

  • ILP. why we need it. Describe a task where ILP is useful


Materials for the labs in week 08:


LAB 04 document clustering embeddings
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