Supervised machine learning has a few recurring concepts: data instance, feature set and label. Often, a data instance has one feature set and one label. But there are situations when you have multi-[X], where X = instance, view (feature subset), or label. For example, in multiple instance learning, you have more then one instance, but only one label.
Things are getting interesting when you have multiple instances, multiple views and multiple labels at the same time. For example, a video clip can be considered as a set of video segments (instances), each of which has views (audio, visual frames and may be textual subtitle), and the clip has many tags (labels).
Enter Column Bundle (CLB), the latest invention in my group.
CLB makes use of the concept of columns in neocortex. In brain, neurons are arranged in thin mini-columns, each of which is thought to cover a small sensory area called receptive field. Mini-columns are bundled into super-columns, which are inter-connected to form the entire neocortex. In our previous work, this cute concept has been exploited to build a network of columns for collective classification. For CLB, columns are arranged in a special way:
- There is one central column that serves as the main processing unit (CPU).
- There are input mini-columns to read inputs for multiple parts (Input)
- There are output mini-columns to generate labels (Output)
- Mini-columns are only connected to the central column.
Columns are recurrent neural nets with skip-connections (e.g., Highway Net, Residual Net or LSTM). Input parts can be instances, or views. The difference is only at the feature mapping: different views are first mapped into the same space.
In a sense, it looks like a neural computer without a RAM.
- On Size Fit Many: Column Bundle for Multi-X Learning, Trang Pham, Truyen Tran, Svetha Venkatesh. arXiv preprint, arXiv:1702.07021
- Column Networks for Collective Classification, T Pham, T Tran, D Phung, S Venkatesh, AAAI'17