Readings

Some interesting books and papers.

Readings on programming (in Czech)

Books

(in descending order of publication date)

Machine Learning

• Sutton et al. (2018): Reinforcement Learning: An Introduction
• Goodfellow et al. (2016): Deep Learning
• Fink (2007): Markov Models for Pattern Recognition: From Theory to Applications

Python

• Ramalho (2022): Fluent Python, 2nd Edition
• Slatkin (2019): Effective Python: 2nd Edition

Computer Science

• Mareš et al. (2017): Průvodce labyrintem algoritmů

Papers

(in descending order of publication date)

• Zhang et al. (2020): Revisiting Graph Neural Networks for Link Prediction
• Cai et al. (2020): Line Graph Neural Networks for Link Prediction
• Hu et al. (2020): GPT-GNN: Generative Pre-Training of Graph Neural Networks
• Dwivedi et al. (2020): Benchmarking Graph Neural Networks
• Grattarola et al. (2020): Graph Neural Networks in TensorFlow and Keras with Spektral
• Rossi et al. (2020): Temporal Graph Networks for Deep Learning on Dynamic Graphs
• Adiwardana et al. (2020): Towards a Human-like Open-Domain Chatbot
• Wu et al. (2020): A Comprehensive Survey on Graph Neural Networks
• Hu et al. (2020): Strategies for Pre-Training Graph Neural Networks
• Bogoychev et al. (2019): Domain, Translationese and Noise in Synthetic Data for Neural Machine Translation
• Holubova et al. (2019): Inferred Social Networks: A Case Study
• Provilkov et al. (2019): BPE-Dropout: Simple and Effective Subword Regularization
• Lan et al. (2019): ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
• Gan et al. (2019): A Survey of Utility-Oriented Pattern Mining
• Gao et al. (2019): Neural Approaches to Conversational AI
• Ghazvininejad et al. (2019): Mask-Predict: Parallel Decoding of Conditional Masked Language Models
• Kondratyuk et al. (2019): 75 Languages, 1 Model: Parsing Universal Dependencies Universally
• Chiang et al. (2019): Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks
• Budzianowski et al. (2019): Hello, It’s GPT-2 – How Can I Help You? Towards the Use of Pretrained Language Models for Task-Oriented Dialogue Systems
• Zhou et al. (2019): Graph Neural Networks: A Review of Methods and Applications
• Yang et al. (2019): XLNet: Generalized Autoregressive Pretraining for Language Understanding
• Wang et al. (2019): Knowledge-aware Graph Neural Networks with Label Smoothness Regularization for Recommender Systems
• Sennrich et al. (2019): Revisiting Low-Resource Neural Machine Translation: A Case Study
• So et al. (2019): The Evolved Transformer
• Voita et al. (2019): When a Good Translation is Wrong in Context: Context-Aware Machine Translation Improves on Deixis, Ellipsis, and Lexical Cohesion
• Xu et al. (2019): How Powerful are Graph Neural Networks?
• Real et al. (2019): Regularized Evolution for Image Classifier Architecture Search
• Lample et al. (2019): Cross-lingual Language Model Pretraining
• Dai et al. (2019): Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context
• Bhanja et al. (2019): Impact of Data Normalization on Deep Neural Network for Time Series Forecasting
• Radford et al. (2019): Language Models are Unsupervised Multitask Learners
• Zhang et al. (2018): Link Prediction Based on Graph Neural Networks
• Porwal et al. (2018): Credit Card Fraud Detection in e-Commerce: An Outlier Detection Approach
• Devlin et al. (2018): BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
• Edunov et al. (2018): Understanding Back-Translation at Scale
• Li et al. (2018): Anomaly Detection with Generative Adversarial Networks for Multivariate Time Series
• Kudo et al. (2018): SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing
• Wang et al. (2018): Anomaly Detection via Minimum Likelihood Generative Adversarial Networks
• Hamilton et al. (2018): Representation Learning on Graphs: Methods and Applications
• Popel et al. (2018): Training Tips for the Transformer Model
• Grave et al. (2018): Learning Word Vectors for 157 Languages
• Zenati et al. (2018): Efficient GAN-Based Anomaly Detection
• Peters et al. (2018): Deep contextualized word representations
• Espeholt et al. (2018): IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures
• Veličković et al. (2018): Graph Attention Networks
• Gatt et al. (2018): Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation
• Fotso (2018): Deep Neural Networks for Survival Analysis Based on a Multi-Task Framework
• Zhang et al. (2018): Link Prediction Based on Graph Neural Networks
• Silver et al. (2018): A general reinforcement learning algorithm that masters chess, shogi and Go through self-play
• Radford et al. (2018): Improving Language Understanding by Generative Pre-Training
• Lei et al. (2018): Sequicity: Simplifying Task-oriented Dialogue Systems with Single Sequence-to-Sequence Architectures
• Mikolov et al. (2017): Advances in Pre-Training Distributed Word Representations
• Schlichtkrull et al. (2017): Modeling Relational Data with Graph Convolutional Networks
• Schlichtkrull et al. (2017): Modeling Relational Data with Graph Convolutional Networks
• Hessel et al. (2017): Rainbow: Combining Improvements in Deep Reinforcement Learning
• Jia et al. (2017): Adversarial Examples for Evaluating Reading Comprehension Systems
• Fortunato et al. (2017): Noisy Networks for Exploration
• Vaswani et al. (2017): Attention Is All You Need
• Gilmer et al. (2017): Neural Message Passing for Quantum Chemistry
• Clemente et al. (2017): Efficient Parallel Methods for Deep Reinforcement Learning
• Williams et al. (2017): Hybrid Code Networks: practical and efficient end-to-end dialog control with supervised and reinforcement learning
• Gulrajani et al. (2017): Improved Training of Wasserstein GANs
• Schlegl et al. (2017): Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery
• Kipf et al. (2017): Semi-Supervised Classification with Graph Convolutional Networks
• Arjovsky et al. (2017): Wasserstein GAN
• Shaikhina et al. (2017): Handling limited datasets with neural networks in medical applications: A small-data approach
• Kipf et al. (2016): Variational Graph Auto-Encoders
• Sennrich et al. (2016): Neural Machine Translation of Rare Words with Subword Units
• Salimans et al. (2016): Improved Techniques for Training GANs
• Sennrich et al. (2016): Improving Neural Machine Translation Models with Monolingual Data
• Dumoulin et al. (2016): Adversarially Learned Inference
• Donahue et al. (2016): Adversarial Feature Learning
• Bahdanau et al. (2016): Neural Machine Translation by Jointly Learning to Align and Translate
• Mnih et al. (2016): Asynchronous Methods for Deep Reinforcement Learning
• Chigurupati et al. (2016): Predicting hardware failure using machine learning
• Wang et al. (2015): Dueling Network Architectures for Deep Reinforcement Learning
• Radford et al. (2015): Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
• van Hasselt et al. (2015): Deep Reinforcement Learning with Double Q-learning
• Lillicrap et al. (2015): Continuous control with deep reinforcement learning
• Ling et al. (2015): Finding Function in Form: Compositional Character Models for Open Vocabulary Word Representation
• Mnih et al. (2015): Human-level control through deep reinforcement learning
• Malhotra et al. (2015): Long Short Term Memory Networks for Anomaly Detection in Time Series
• Sutskever et al. (2014): Sequence to Sequence Learning with Neural Networks
• Goodfellow et al. (2014): Generative Adversarial Networks
• Kingma et al. (2014): Auto-Encoding Variational Bayes
• Shlens (2014): A Tutorial on Principal Component Analysis
• Silver et al. (2014): Deterministic Policy Gradient Algorithms
• Nasreen et al. (2014): Frequent Pattern Mining Algorithms for Finding Associated Frequent Patterns for Data Streams: A Survey
• Mikolov et al. (2013): Distributed Representations of Words and Phrases and their Compositionality
• Mikolov et al. (2013): Efficient Estimation of Word Representations in Vector Space
• Jurish et al. (2013): Word and Sentence Tokenization with Hidden Markov Models
• Surdeanu et al. (2011): Customizing an Information Extraction System to a New Domain
• Chandola et al. (2009): Anomaly detection: A survey
• Yen et al. (2009): An Efficient Algorithm for Maintaining Frequent Closed Itemsets over Data Stream
• Knight (2009): Bayesian Inference with Tears
• Nadeau et al. (2006): A survey of named entity recognition and classification
• Graves et al. (2006): Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks
• Cucerzan (2006): Large-Scale Named Entity Disambiguation Based on Wikipedia Data
• Liben-Nowell et al. (2003): The Link Prediction Problem for Social Networks
• Haag et al. (2003): In search of the benefits of learning Latin.
• Cucerzan et al. (2003): Minimally Supervised Induction of Grammatical Gender
• Cucerzan et al. (2002): Bootstrapping a multilingual part-of-speech tagger in one person-day
• Goldsmith (2001): Unsupervised Learning of the Morphology of a Natural Language
• Schone et al. (2001): Knowledge-free induction of inflectional morphologies
• Yarowsky et al. (2000): Minimally supervised morphological analysis by multimodal alignment
• Fillmore (1991): When learning a second language means losing the first
• Kaushanskaya (1991): The Effect of Second-Language Experience on Native-Language Processing