Generalizing from a few examples: A survey on few-shot learning
Machine learning has been highly successful in data-intensive applications but is often
hampered when the data set is small. Recently, Few-shot Learning (FSL) is proposed to …
hampered when the data set is small. Recently, Few-shot Learning (FSL) is proposed to …
Transfer learning in robotics: An upcoming breakthrough? A review of promises and challenges
Transfer learning is a conceptually-enticing paradigm in pursuit of truly intelligent embodied
agents. The core concept—reusing prior knowledge to learn in and from novel situations—is …
agents. The core concept—reusing prior knowledge to learn in and from novel situations—is …
Few-shot class-incremental learning by sampling multi-phase tasks
New classes arise frequently in our ever-changing world, eg, emerging topics in social
media and new types of products in e-commerce. A model should recognize new classes …
media and new types of products in e-commerce. A model should recognize new classes …
Reconciling meta-learning and continual learning with online mixtures of tasks
Learning-to-learn or meta-learning leverages data-driven inductive bias to increase the
efficiency of learning on a novel task. This approach encounters difficulty when transfer is …
efficiency of learning on a novel task. This approach encounters difficulty when transfer is …
Intelligent fault diagnosis under small sample size conditions via Bidirectional InfoMax GAN with unsupervised representation learning
The abnormal detection of rotating machinery under small sample size conditions is of prime
importance in the field of fault diagnosis. In this work, we proposed an unsupervised …
importance in the field of fault diagnosis. In this work, we proposed an unsupervised …
Multi-information fusion fault diagnosis of bogie bearing under small samples via unsupervised representation alignment deep Q-learning
Y Zhu, X Liang, T Wang, J Xie… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
With the ever-accelerating development of information and sensor technology, plenty of data-
driven fault diagnosis algorithms have shown impressive performance. However, in practical …
driven fault diagnosis algorithms have shown impressive performance. However, in practical …
Few-shot learning with adaptively initialized task optimizer: a practical meta-learning approach
Considering the data collection and labeling cost in real-world applications, training a model
with limited examples is an essential problem in machine learning, visual recognition, etc …
with limited examples is an essential problem in machine learning, visual recognition, etc …
Task-agnostic online reinforcement learning with an infinite mixture of gaussian processes
Continuously learning to solve unseen tasks with limited experience has been extensively
pursued in meta-learning and continual learning, but with restricted assumptions such as …
pursued in meta-learning and continual learning, but with restricted assumptions such as …
Distributionally adaptive meta reinforcement learning
Meta-reinforcement learning algorithms provide a data-driven way to acquire policies that
quickly adapt to many tasks with varying rewards or dynamics functions. However, learned …
quickly adapt to many tasks with varying rewards or dynamics functions. However, learned …
What can knowledge bring to machine learning?—a survey of low-shot learning for structured data
Supervised machine learning has several drawbacks that make it difficult to use in many
situations. Drawbacks include heavy reliance on massive training data, limited …
situations. Drawbacks include heavy reliance on massive training data, limited …