ICML, NeurIPS), and generally provide you with the guidance you need to contribute to ML research fully and effectively! Few-Shot Learning Neural networks have been successfully applied in applications with a large amount of labeled data. A summary of meta learning papers based on taxonomic category. Our biggest goal is to help you publish papers at top conferences (e.g. Sorted by submitted date on arXiv. Background • two level learning on meta learning: • slow learning of a meta-level model preforming across tasks • rapid learning of a base-level model acting with each task 2 Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks. In effect, our method trains the model to be easy to fine-tune. (NTMs) (Graves et al., 2014) and memory networks (We-stonetal.,2014), meettherequisitecriteria. The goal of meta-learning is to train a model on a variety of learning tasks, such that it can solve new learning tasks using only a small number of training samples. In our approach, the parameters of the model are explicitly trained such that a small number of gradient steps with a small amount of training data from a new task will produce good generalization performance on that task. This can pose a major challenge for domains r In International Conference on Machine Learning (ICML), 2015. Meta-Learning Papers. Google Scholar; Naik, Devang K and Mammone, RJ. Heterogeneous Networks Yuxiao Dong ... the other has 10 publications all in ICML; their “APCPA”-based Path-Sim similarity [26] would be zero—this will be naturally overcome by network representation learning. Meta Network ICML 2017 citation: 0 Tsendsuren Munkhdalai Hong Yu University of Massachusetts, MA, USA Katy Lee @ Datalab 2017.09.11 1 2. Andso, inthis paper we revisit the meta-learning problem and setup from the perspective of a highly capable memory-augmented neural network (MANN) (note: here on, the term MANN will refer to the class of external-memory equipped net- Meta networks. International Conferecence on Machine Learning (ICML), 2017. Search this site p Meta-Learning: from Few-Shot Learning to Rapid Reinforcement Learning ICML 2019 Tutorial Abstract In recent years, high-capacity models, such as deep neural networks, have enabled very powerful machine learning techniques in domains where data is plentiful.
To manage your alert preferences, click on the button below.University of California, BerkeleyCheck if you have access through your login credentials or your institution to get full access on this article.We propose an algorithm for meta-learning that is model-agnostic, in the sense that it is compatible with any model trained with gradient descent and applicable to a variety of different learning problems, including classification, regression, and reinforcement learning. Electronic Proceedings of the 34th International Conference on Machine Learning.
However, the task of rapid generalization on new concepts with small training data while preserving performances on previously learned ones still presents a significant challenge to neural network models. Meta-Learning in Neural Networks: A Survey []Timothy Hospedales, Antreas Antoniou, Paul Micaelli, Amos Storkey
“universal function approximator” Recurrent network Learned optimizer “universal learning procedure approximator” Finn, Abbeel Levine. How can we define a notion of expressive power for meta-learning? Survey. ICML 2017 DBLP Scholar?EE?
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