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