36 citations · 112 across the 14 of their papers we have counts for
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cs.LG2018★ 20 cited
Multi-agent Deep Reinforcement Learning with Extremely Noisy Observations
Ozsel Kilinc, Giovanni Montana
Multi-agent reinforcement learning systems aim to provide interacting agents with the ability to collaboratively learn and adapt to the behaviour of other agents. In many real-worl…
cs.LG2018★ 23 cited
Learning Latent Representations in Neural Networks for Clustering through Pseudo Supervision and Graph-based Activity Regularization
Ozsel Kilinc, Ismail Uysal
In this paper, we propose a novel unsupervised clustering approach exploiting the hidden information that is indirectly introduced through a pseudo classification objective. Specif…