16 citations · 24 across the 4 of their papers we have counts for
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cs.CL2019
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…