Výsledky vyhledávání - "Vithanage, Kasun"

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End-To-End Data-Dependent Routing in Multi-Path Neural Networks

Autor: Tissera, Dumindu, Wijessinghe, Rukshan, Vithanage, Kasun, Xavier, Alex, Fernando, Subha, Rodrigo, Ranga

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 established, there is still room for efficient feature extraction within

Externí odkaz: http://arxiv.org/abs/2107.02450

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Neural Mixture Models with Expectation-Maximization for End-to-end Deep Clustering

Autor: Tissera, Dumindu, Vithanage, Kasun, Wijesinghe, Rukshan, Xavier, Alex, Jayasena, Sanath, Fernando, Subha, Rodrigo, Ranga

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 with pre-defined statistical distributions and allocate data to those clu

Externí odkaz: http://arxiv.org/abs/2107.02453

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Transferring Domain Knowledge with an Adviser in Continuous Tasks

Autor: Wijesinghe, Rukshan, Vithanage, Kasun, Tissera, Dumindu, Xavier, Alex, Fernando, Subha, Samarawickrama, Jayathu

Recent advances in Reinforcement Learning (RL) have surpassed human-level performance in many simulated environments. However, existing reinforcement learning techniques are incapable of explicitly incorporating already known domain-specific knowledg

Externí odkaz: http://arxiv.org/abs/2102.08029

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Feature-Dependent Cross-Connections in Multi-Path Neural Networks

Autor: Tissera, Dumindu, Vithanage, Kasun, Wijesinghe, Rukshan, Kahatapitiya, Kumara, Fernando, Subha, Rodrigo, Ranga

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. As opposed to conventional network widening, multi-path architecture

Externí odkaz: http://arxiv.org/abs/2006.13904

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