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Jay Heo

2 accepted papers

2020

Cost-Effective Interactive Attention Learning with Neural Attention Processes

ICML 2020poster

We propose a novel interactive learning framework which we refer to as Interactive Attention Learning (IAL), in which the human supervisors interactively manipulate the allocated attentions, to correct the model’s behaviour by updating the attention-generating network. However, such a model is prone…

2018

Uncertainty-Aware Attention for Reliable Interpretation and Prediction

NeurIPS 2018poster

Attention mechanism is effective in both focusing the deep learning models on relevant features and interpreting them. However, attentions may be unreliable since the networks that generate them are often trained in a weakly-supervised manner. To overcome this limitation, we introduce the notion of…