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Black-box machine learning (ML) models have become increasingly popular in practice. They can offer great performance, especially in …
In this work, we introduce models that perform comparably with state-of-the-art alternatives in terms of prediction accuracy while …
As producing high-quality summaries of chat dialogues currently requires large labeled datasets, we propose a method to efficiently …
The estimation of time-varying quantities is a fundamental component of decision making in fields such as healthcare and finance. …
End-to-end interactive learning of dialogue systems has been all-but-abandoned in favour of other approaches using more labelled data, …
In this paper, we explore the use of GAN-based few-shot data augmentation as a method to improve few-shot classification performance. …
A popular approach for sequential decision-making is to perform simulator-based search
guided with Machine Learning (ML) methods like …
We explore the downstream task performances for graph neural network (GNN) self-supervised learning (SSL) methods trained on subgraphs …
Reinforcement learning (RL) aims at autonomously performing complex tasks. To this end, a reward signal is used to steer the learning …
The ALFRED environment features embodied instruction following tasks in simulated home environments. However, end-to-end deep learning …