77 citations · 108 across the 6 of their papers we have counts for
Showing 2019Show all
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cs.CL2019★ 77 cited
On Identifiability in Transformers
Gino Brunner, Yang Liu, Damián Pascual +3
In this paper we delve deep in the Transformer architecture by investigating two of its core components: self-attention and contextual embeddings. In particular, we study the ident…
cs.LG2019
Attentive Multi-Task Deep Reinforcement Learning
Timo Bram, Gino Brunner, Oliver Richter +1
Sharing knowledge between tasks is vital for efficient learning in a multi-task setting. However, most research so far has focused on the easier case where knowledge transfer is no…
cs.LG2019★ 1 cited
Learning Policies through Quantile Regression
Oliver Richter, Roger Wattenhofer
Policy gradient based reinforcement learning algorithms coupled with neural networks have shown success in learning complex policies in the model free continuous action space contr…