An attention-augmented actor-critic agent learns to dynamically weight multiple environment views by importance and outperforms baselines on TORCS and three other 3D simulators under noise and partial observability.
Neural machine translation by jointly learning to align and translate
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2019 2verdicts
UNVERDICTED 2representative citing papers
SBSG model generates sequences bidirectionally from ends to middle via interactive attention, claiming faster decoding and better quality than autoregressive Transformer on NMT and summarization tasks.
citing papers explorer
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An Actor-Critic-Attention Mechanism for Deep Reinforcement Learning in Multi-view Environments
An attention-augmented actor-critic agent learns to dynamically weight multiple environment views by importance and outperforms baselines on TORCS and three other 3D simulators under noise and partial observability.
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Sequence Generation: From Both Sides to the Middle
SBSG model generates sequences bidirectionally from ends to middle via interactive attention, claiming faster decoding and better quality than autoregressive Transformer on NMT and summarization tasks.