Wide & Deep Learning combines the benefits of memorization and generalization in machine learning models to improve performance in tasks such as recommender systems.
Wide & Deep Learning is a technique that combines wide linear models and deep neural networks to achieve better performance in tasks like recommender systems. This approach takes advantage of the memorization capabilities of wide models, which capture feature interactions through cross-product transformations, and the generalization capabilities of deep models, which learn low-dimensional dense embeddings for sparse features. By jointly training these two components, Wide & Deep Learning can provide more accurate and relevant recommendations, especially in cases where user-item interactions are sparse and high-rank.
Recent research in this area has explored various aspects of Wide & Deep Learning, such as quantum deep learning, distributed deep reinforcement learning, and deep active learning. Quantum deep learning investigates the use of quantum computing techniques for training deep neural networks, while distributed deep reinforcement learning focuses on improving sample efficiency and scalability in multi-agent environments. Deep active learning, on the other hand, aims to bridge the gap between theoretical findings and practical applications by leveraging training dynamics for better generalization performance.
Practical applications of Wide & Deep Learning can be found in various domains, such as mobile app stores, robot swarm control, and machine health monitoring. For example, Google Play, a commercial mobile app store with over one billion active users and over one million apps, has successfully implemented Wide & Deep Learning to significantly increase app acquisitions compared to wide-only and deep-only models. In robot swarm control, the Wide and Deep Graph Neural Networks (WD-GNN) architecture has been proposed for distributed online learning, showing potential for real-world applications. In machine health monitoring, deep learning techniques have been employed to process and analyze large amounts of data collected from sensors in modern manufacturing systems.
In conclusion, Wide & Deep Learning is a promising approach that combines the strengths of both wide linear models and deep neural networks to improve performance in various tasks, particularly in recommender systems. By exploring different aspects of this technique, such as quantum deep learning, distributed deep reinforcement learning, and deep active learning, researchers are continually pushing the boundaries of what is possible with Wide & Deep Learning and its applications in real-world scenarios.
Wide & Deep Learning
Wide & Deep Learning Further Reading
1.Quantum Neural Networks: Concepts, Applications, and Challenges http://arxiv.org/abs/2108.01468v1 Yunseok Kwak, Won Joon Yun, Soyi Jung, Joongheon Kim2.Distributed Deep Reinforcement Learning: A Survey and A Multi-Player Multi-Agent Learning Toolbox http://arxiv.org/abs/2212.00253v1 Qiyue Yin, Tongtong Yu, Shengqi Shen, Jun Yang, Meijing Zhao, Kaiqi Huang, Bin Liang, Liang Wang3.Generalization and Expressivity for Deep Nets http://arxiv.org/abs/1803.03772v2 Shao-Bo Lin4.DOC3-Deep One Class Classification using Contradictions http://arxiv.org/abs/2105.07636v2 Sauptik Dhar, Bernardo Gonzalez Torres5.An Overview of Deep Semi-Supervised Learning http://arxiv.org/abs/2006.05278v2 Yassine Ouali, Céline Hudelot, Myriam Tami6.Wide and Deep Graph Neural Networks with Distributed Online Learning http://arxiv.org/abs/2006.06376v2 Zhan Gao, Fernando Gama, Alejandro Ribeiro7.Wide & Deep Learning for Recommender Systems http://arxiv.org/abs/1606.07792v1 Heng-Tze Cheng, Levent Koc, Jeremiah Harmsen, Tal Shaked, Tushar Chandra, Hrishi Aradhye, Glen Anderson, Greg Corrado, Wei Chai, Mustafa Ispir, Rohan Anil, Zakaria Haque, Lichan Hong, Vihan Jain, Xiaobing Liu, Hemal Shah8.Deep Active Learning by Leveraging Training Dynamics http://arxiv.org/abs/2110.08611v2 Haonan Wang, Wei Huang, Ziwei Wu, Andrew Margenot, Hanghang Tong, Jingrui He9.The large learning rate phase of deep learning: the catapult mechanism http://arxiv.org/abs/2003.02218v1 Aitor Lewkowycz, Yasaman Bahri, Ethan Dyer, Jascha Sohl-Dickstein, Guy Gur-Ari10.Deep Learning and Its Applications to Machine Health Monitoring: A Survey http://arxiv.org/abs/1612.07640v1 Rui Zhao, Ruqiang Yan, Zhenghua Chen, Kezhi Mao, Peng Wang, Robert X. GaoWide & Deep Learning Frequently Asked Questions
What is wide and deep learning?
Wide & Deep Learning is a machine learning technique that combines wide linear models and deep neural networks to achieve better performance in tasks like recommender systems. It takes advantage of the memorization capabilities of wide models, which capture feature interactions through cross-product transformations, and the generalization capabilities of deep models, which learn low-dimensional dense embeddings for sparse features. By jointly training these two components, Wide & Deep Learning can provide more accurate and relevant recommendations, especially in cases where user-item interactions are sparse and high-rank.
What is deep vs wide machine learning?
Deep machine learning refers to the use of deep neural networks, which consist of multiple layers of interconnected neurons, to learn complex patterns and representations from data. These models are known for their ability to generalize well, meaning they can make accurate predictions on new, unseen data. On the other hand, wide machine learning involves the use of wide linear models, which capture feature interactions through cross-product transformations. Wide models are known for their memorization capabilities, meaning they can effectively learn and store specific patterns in the training data.
What is wide and deep learning for classification?
Wide & Deep Learning can be applied to classification tasks, where the goal is to predict the class or category of an input. In this context, the wide component of the model captures feature interactions and memorizes specific patterns in the training data, while the deep component learns low-dimensional dense embeddings for sparse features, allowing for better generalization. By combining these two components, Wide & Deep Learning can improve classification performance, particularly in cases where the input data is sparse and high-dimensional.
What is the difference between deep and wide network?
A deep network refers to a neural network with multiple layers of interconnected neurons, allowing it to learn complex patterns and representations from data. Deep networks are known for their ability to generalize well, making accurate predictions on new, unseen data. In contrast, a wide network typically refers to a wide linear model, which captures feature interactions through cross-product transformations. Wide networks are known for their memorization capabilities, effectively learning and storing specific patterns in the training data.
How does wide and deep learning improve recommender systems?
Wide & Deep Learning improves recommender systems by combining the strengths of both wide linear models and deep neural networks. The wide component captures feature interactions and memorizes specific patterns in the training data, while the deep component learns low-dimensional dense embeddings for sparse features, allowing for better generalization. By jointly training these two components, Wide & Deep Learning can provide more accurate and relevant recommendations, especially in cases where user-item interactions are sparse and high-rank.
What are some practical applications of wide and deep learning?
Practical applications of Wide & Deep Learning can be found in various domains, such as mobile app stores, robot swarm control, and machine health monitoring. For example, Google Play, a commercial mobile app store with over one billion active users and over one million apps, has successfully implemented Wide & Deep Learning to significantly increase app acquisitions compared to wide-only and deep-only models. In robot swarm control, the Wide and Deep Graph Neural Networks (WD-GNN) architecture has been proposed for distributed online learning, showing potential for real-world applications. In machine health monitoring, deep learning techniques have been employed to process and analyze large amounts of data collected from sensors in modern manufacturing systems.
What are some recent research directions in wide and deep learning?
Recent research in Wide & Deep Learning has explored various aspects, such as quantum deep learning, distributed deep reinforcement learning, and deep active learning. Quantum deep learning investigates the use of quantum computing techniques for training deep neural networks, while distributed deep reinforcement learning focuses on improving sample efficiency and scalability in multi-agent environments. Deep active learning, on the other hand, aims to bridge the gap between theoretical findings and practical applications by leveraging training dynamics for better generalization performance.
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