prepare an annotated bibliography of 8-10 articles that you will gather for your research proposal for comparative analysis of deep learning, reinforced learning, and natural language processing .Your references must be from peer-reviewed articles .APA 7th Edition applies and makes sure that you follow: APA Purdue.
1. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT press.
This comprehensive book by Goodfellow, Bengio, and Courville provides a thorough understanding of deep learning techniques. It covers various topics related to deep learning, including neural networks, optimization algorithms, and generative models. The authors discuss the theoretical foundations of deep learning and its practical applications in fields like computer vision and natural language processing. This book will serve as an excellent resource for gaining a solid understanding of deep learning concepts.
2. Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., … & Petersen, S. (2015). Human-level control through deep reinforcement learning. Nature, 518(7540), 529-533.
In this groundbreaking article, Mnih et al. introduce a novel approach to reinforcement learning using deep neural networks. They demonstrate that their deep Q-network (DQN) algorithm can outperform human experts in several Atari 2600 games, achieving human-level control. The authors propose a new architecture that combines neural networks with the Q-learning algorithm, enabling more efficient and effective reinforcement learning. This article showcases the power of deep reinforcement learning in achieving significant advancements in artificial intelligence.
3. Mikolov, T., Karafiát, M., Burget, L., Černocký, J., & Khudanpur, S. (2010). Recurrent neural network based language model. In Interspeech (Vol. 2, No. 3, p. 1045).
Mikolov et al. explore the application of recurrent neural networks (RNNs) in language modeling. They propose an efficient architecture called the RNN-based language model, which can capture long-range dependencies in sequential data. The authors compare their model with traditional n-gram models and demonstrate its superior performance in various language modeling tasks. This article provides valuable insights into the use of RNNs for natural language processing tasks.
4. Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K., & Kuksa, P. (2011). Natural language processing (almost) from scratch. Journal of Machine Learning Research, 12(Aug), 2493-2537.
Collobert et al. present a deep neural network architecture for natural language processing tasks. Their model, known as the Convolutional Neural Network (CNN), demonstrates impressive performance in various NLP tasks, including named entity recognition and part-of-speech tagging. The authors emphasize the advantages of end-to-end learning and the minimal requirements for pre-processing. This article serves as a foundational piece for understanding deep learning approaches in NLP.
5. Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., Van Den Driessche, G., … & Dieleman, S. (2016). Mastering the game of Go with deep neural networks and tree search. Nature, 529(7587), 484-489.
Silver et al. present AlphaGo, a system that combines deep neural networks with Monte Carlo tree search to achieve superhuman performance in the game of Go. The authors demonstrate how deep learning techniques can be applied to complex games requiring strategic decision-making. This article highlights the potential of deep learning and reinforcement learning in tackling challenging problems in decision-making domains.
6. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., … & Polosukhin, I. (2017). Attention is all you need. In Advances in neural information processing systems (pp. 5998-6008).
Vaswani et al. propose a revolutionary transformer architecture for natural language processing, eliminating the need for recurrent or convolutional neural networks. The transformer model achieves state-of-the-art performance on machine translation tasks, relying solely on attention mechanisms. This paper discusses the advantages of the transformer architecture and its potential for further advancements in NLP.
7. Mnih, V., Badia, A. P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., … & Silver, D. (2016). Asynchronous methods for deep reinforcement learning. In International conference on machine learning (pp. 1928-1937).
Mnih et al. investigate the use of asynchronous methods in deep reinforcement learning. They propose asynchronous advantage actor-critic (A3C), a highly efficient method for training deep RL agents through parallelization. The authors demonstrate significant speed improvements compared to traditional reinforcement learning algorithms. This research contributes to the field by addressing the challenge of scalability in deep RL.
8. Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805.
Devlin et al. introduce BERT, a pre-training technique for NLP that utilizes bidirectional transformers. BERT achieves state-of-the-art results on a wide range of language understanding tasks, including question answering and sentiment analysis. The authors explore different pre-training objectives and demonstrate the effectiveness of BERT in learning contextual representations. This article highlights the significance of pre-training techniques in advancing natural language understanding.