6 citations · 6 across the 3 of their papers we have counts for
5 papers
Neural Mixture Models with Expectation-Maximization for End-to-end Deep Clustering
Dumindu Tissera, Kasun Vithanage, Rukshan Wijesinghe +4
Any clustering algorithm must synchronously learn to model the clusters and allocate data to those clusters in the absence of labels. Mixture model-based methods model clusters wit…
End-To-End Data-Dependent Routing in Multi-Path Neural Networks
Dumindu Tissera, Rukshan Wijessinghe, Kasun Vithanage +3
Neural networks are known to give better performance with increased depth due to their ability to learn more abstract features. Although the deepening of networks has been well est…
Transferring Domain Knowledge with an Adviser in Continuous Tasks
Rukshan Wijesinghe, Kasun Vithanage, Dumindu Tissera +3
Recent advances in Reinforcement Learning (RL) have surpassed human-level performance in many simulated environments. However, existing reinforcement learning techniques are incapa…
Feature-Dependent Cross-Connections in Multi-Path Neural Networks
Dumindu Tissera, Kasun Vithanage, Rukshan Wijesinghe +3
Learning a particular task from a dataset, samples in which originate from diverse contexts, is challenging, and usually addressed by deepening or widening standard neural networks…
Context-Aware Multipath Networks
Dumindu Tissera, Kumara Kahatapitiya, Rukshan Wijesinghe +2
Making a single network effectively address diverse contexts---learning the variations within a dataset or multiple datasets---is an intriguing step towards achieving generalized i…