110 citations · 136 across the 5 of their papers we have counts for
5 papers
A Model for Intelligible Interaction Between Agents That Predict and Explain
A. Baskar, Ashwin Srinivasan, Michael Bain +1
Machine Learning (ML) has emerged as a powerful form of data modelling with widespread applicability beyond its roots in the design of autonomous agents. However, relatively little…
One-way Explainability Isn't The Message
Ashwin Srinivasan, Michael Bain, Enrico Coiera
Recent engineering developments in specialised computational hardware, data-acquisition and storage technology have seen the emergence of Machine Learning (ML) as a powerful form o…
Logical Explanations for Deep Relational Machines Using Relevance Information
Ashwin Srinivasan, Lovekesh Vig, Michael Bain
Our interest in this paper is in the construction of symbolic explanations for predictions made by a deep neural network. We will focus attention on deep relational machines (DRMs,…
B-CNN: Branch Convolutional Neural Network for Hierarchical Classification
Xinqi Zhu, Michael Bain
Convolutional Neural Network (CNN) image classifiers are traditionally designed to have sequential convolutional layers with a single output layer. This is based on the assumption…
Word Vector Enrichment of Low Frequency Words in the Bag-of-Words Model for Short Text Multi-class Classification Problems
Bradford Heap, Michael Bain, Wayne Wobcke +2
The bag-of-words model is a standard representation of text for many linear classifier learners. In many problem domains, linear classifiers are preferred over more complex models…