(NTMs) (Graves et al., 2014) and memory networks (We-stonetal.,2014), meettherequisitecriteria. ICML 2017 DBLP Scholar?EE?
Meta-Learning in Neural Networks: A Survey []Timothy Hospedales, Antreas Antoniou, Paul Micaelli, Amos Storkey
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. Various recent meta-learning approaches. Google Scholar; Naik, Devang K and Mammone, RJ. Our biggest goal is to help you publish papers at top conferences (e.g.
In International Conference on Machine Learning (ICML), 2015. However, domains where data is scarce have proven challenging for such methods because high-capacity function approximators critically rely on large datasets for generalization.
Google Scholar Digital Library; Munkhdalai, Tsendsuren and Yu, Hong.
This can pose a major challenge for domains r 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- “universal function approximator” Recurrent network Learned optimizer “universal learning procedure approximator” Finn, Abbeel Levine. Tsendsuren Munkhdalai, Hong Yu 0001 Meta Networks ICML, 2017.
Neural networks have been successfully applied in applications with a large amount of labeled data. 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.
Meta-Learning Papers. Survey.
Sorted by submitted date on arXiv. 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. We demonstrate that this approach leads to state-of-the-art performance on two few-shot image classification benchmarks, produces good results on few-shot regression, and accelerates fine-tuning for policy gradient reinforcement learning with neural network policies.You will be notified whenever a record that you have chosen has been cited.University of California, Berkeley and OpenAIView or Download as a PDF file.https://dl.acm.org/doi/10.5555/3305381.3305498View this article in digital edition.We use cookies to ensure that we give you the best experience on our website.
Meta-neural networks that learn by learning.
ICML, NeurIPS), and generally provide you with the guidance you need to contribute to ML research fully and effectively! July 13th, 2020: Session day; You should be able to join the zoom meeting in icml.cc - workshop - New In ML workshop page. 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 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. Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks. International Conferecence on Machine Learning (ICML), 2017. 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.
ICML 2019 Meta-Learning Tutorial 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 … Recently meta-learning has become a hot topic, with a flurry of recent papers, most commonly using the technique for hyperparameter and neural network optimization, finding good network architectures, few-shot image recognition, and fast reinforcement learning. 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. Important Dates.
Few-Shot Learning
Meta networks. ... meta-path [25] based random walks in heterogeneous networks How can we define a notion of expressive power for meta-learning? Electronic Proceedings of the 34th International Conference on Machine Learning.
In effect, our method trains the model to be easy to fine-tune.
Meta Network ICML 2017 citation: 0 Tsendsuren Munkhdalai Hong Yu University of Massachusetts, MA, USA Katy Lee @ Datalab 2017.09.11 1 2.