98 citations · 214 across the 4 of their papers we have counts for
5 papers
RelationNet2: Deep Comparison Columns for Few-Shot Learning
Xueting Zhang, Yuting Qiang, Flood Sung +2
Few-shot deep learning is a topical challenge area for scaling visual recognition to open ended growth of unseen new classes with limited labeled examples. A promising approach is…
Learning to Compare: Relation Network for Few-Shot Learning
Flood Sung, Yongxin Yang, Li Zhang +3
We present a conceptually simple, flexible, and general framework for few-shot learning, where a classifier must learn to recognise new classes given only few examples from each. O…
Deep Stock Representation Learning: From Candlestick Charts to Investment Decisions
Guosheng Hu, Yuxin Hu, Kai Yang +8
We propose a novel investment decision strategy (IDS) based on deep learning. The performance of many IDSs is affected by stock similarity. Most existing stock similarity measureme…
Learning to Learn: Meta-Critic Networks for Sample Efficient Learning
Flood Sung, Li Zhang, Tao Xiang +2
We propose a novel and flexible approach to meta-learning for learning-to-learn from only a few examples. Our framework is motivated by actor-critic reinforcement learning, but can…
Actor-Critic Sequence Training for Image Captioning
Li Zhang, Flood Sung, Feng Liu +4
Generating natural language descriptions of images is an important capability for a robot or other visual-intelligence driven AI agent that may need to communicate with human users…