10,619 research outputs found
Multimodal Explanations: Justifying Decisions and Pointing to the Evidence
Deep models that are both effective and explainable are desirable in many
settings; prior explainable models have been unimodal, offering either
image-based visualization of attention weights or text-based generation of
post-hoc justifications. We propose a multimodal approach to explanation, and
argue that the two modalities provide complementary explanatory strengths. We
collect two new datasets to define and evaluate this task, and propose a novel
model which can provide joint textual rationale generation and attention
visualization. Our datasets define visual and textual justifications of a
classification decision for activity recognition tasks (ACT-X) and for visual
question answering tasks (VQA-X). We quantitatively show that training with the
textual explanations not only yields better textual justification models, but
also better localizes the evidence that supports the decision. We also
qualitatively show cases where visual explanation is more insightful than
textual explanation, and vice versa, supporting our thesis that multimodal
explanation models offer significant benefits over unimodal approaches.Comment: arXiv admin note: text overlap with arXiv:1612.0475
ï»żAn Answer Explanation Model for Probabilistic Database Queries
Following the availability of huge amounts of uncertain data, coming from diverse ranges of applications such as sensors, machine learning or mining approaches, information extraction and integration, etc. in recent years, we have seen a revival of interests in probabilistic databases. Queries over these databases result in probabilistic answers. As the process of arriving at these answers is based on the underlying stored uncertain data, we argue that from the standpoint of an end user, it is helpful for such a system to give an explanation on how it arrives at an answer and on which uncertainty assumptions the derived answer is based. In this way, the user with his/her own knowledge can decide how much confidence to place in this probabilistic answer. \ud
The aim of this paper is to design such an answer explanation model for probabilistic database queries. We report our design principles and show the methods to compute the answer explanations. One of the main contributions of our model is that it fills the gap between giving only the answer probability, and giving the full derivation. Furthermore, we show how to balance verifiability and influence of explanation components through the concept of verifiable views. The behavior of the model and its computational efficiency are demonstrated through an extensive performance study
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