arXiv: Artificial Intelligence
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#1Marc Hanussek (University of Stuttgart)H-Index: 2
Last. Jens DrawehnH-Index: 1
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With recent progress in the field of Explainable Artificial Intelligence (XAI) and increasing use in practice, the need for an evaluation of different XAI methods and their explanation quality in practical usage scenarios arises. For this purpose, we present VitrAI, which is a web-based service with the goal of uniformly demonstrating four different XAI algorithms in the context of three real life scenarios and evaluating their performance and comprehensibility for humans. This work reveals prac...
#1Fengyu Cai (EPFL: École Polytechnique Fédérale de Lausanne)
#2Wanhao Zhou (EPFL: École Polytechnique Fédérale de Lausanne)
Last. Boi Faltings (EPFL: École Polytechnique Fédérale de Lausanne)H-Index: 64
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Utterance-level intent detection and token-level slot filling are two key tasks for natural language understanding (NLU) in task-oriented systems. Most existing approaches assume that only a single intent exists in an utterance. However, there are often multiple intents within an utterance in real-life scenarios. In this paper, we propose a multi-intent NLU framework, called SLIM, to jointly learn multi-intent detection and slot filling based on BERT. To fully exploit the existing annotation dat...
#1Hongru WangH-Index: 1
Last. Kam-Fai Wong (CUHK: The Chinese University of Hong Kong)H-Index: 36
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Meta-learning is widely used for few-shot slot tagging in the task of few-shot learning. The performance of existing methods is, however, seriously affected by catastrophic forgetting. This phenomenon is common in deep learning as the training and testing modules fail to take into account historical information, i.e. previously trained episodes in the metric-based meta-learning. To overcome this predicament, we propose the Memory-based Contrastive Meta-learning (MCML) method. Specifically, we pr...
1 Citations
#1Giuseppe Placidi (University of L'Aquila)H-Index: 18
#2Luigi CinqueH-Index: 25
Last. Matteo PolsinelliH-Index: 3
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To date, several automated strategies for identification/segmentation of Multiple Sclerosis (MS) lesions by Magnetic Resonance Imaging (MRI) have been presented which are either outperformed by human experts or, at least, whose results are well distinguishable from humans. This is due to the ambiguity originated by MRI instabilities, peculiar MS Heterogeneity and MRI unspecific nature with respect to MS. Physicians partially treat the uncertainty generated by ambiguity relying on personal radiol...
#1Wanpeng Zhang (THU: Tsinghua University)H-Index: 1
#2Xiaoyan CaoH-Index: 1
Last. Xi Xiao (THU: Tsinghua University)H-Index: 11
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Due to the high efficiency and less weather dependency, autonomous greenhouses provide an ideal solution to meet the increasing demand for fresh food. However, managers are faced with some challenges in finding appropriate control strategies for crop growth, since the decision space of the greenhouse control problem is an astronomical number. Therefore, an intelligent closed-loop control framework is highly desired to generate an automatic control policy. As a powerful tool for optimal control, ...
#1Bastian Pfeifer (Medical University of Graz)H-Index: 4
#2Anna SarantiH-Index: 8
Last. Andreas HolzingerH-Index: 2
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Network-based algorithms are used in most domains of research and industry in a wide variety of applications and are of great practical use. In this work, we demonstrate subnetwork detection based on multi-modal node features using a new Greedy Decision Forest for better interpretability. The latter will be a crucial factor in retaining experts and gaining their trust in such algorithms in the future. To demonstrate a concrete application example, we focus in this paper on bioinformatics and sys...
#2Tim Verbelen (UGent: Ghent University)H-Index: 15
Last. Bart DhoedtH-Index: 37
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Although modern object detection and classification models achieve high accuracy, these are typically constrained in advance on a fixed train set and are therefore not flexible to deal with novel, unseen object categories. Moreover, these models most often operate on a single frame, which may yield incorrect classifications in case of ambiguous viewpoints. In this paper, we propose an active inference agent that actively gathers evidence for object classifications, and can learn novel object cat...
#1Leopoldo Bertossi (UAI: Adolfo Ibáñez University)H-Index: 29
There are some recent approaches and results about the use of answer-set programming for specifying counterfactual interventions on entities under classification, and reasoning about them. These approaches are flexible and modular in that they allow the seamless addition of domain knowledge. Reasoning is enabled by query answering from the answer-set program. The programs can be used to specify and compute responsibility-based numerical scores as attributive explanations for classification resul...
#1Giuseppe Marra (Katholieke Universiteit Leuven)H-Index: 14
Last. Luc De RaedtH-Index: 68
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Neural-symbolic and statistical relational artificial intelligence both integrate frameworks for learning with logical reasoning. This survey identifies several parallels across seven different dimensions between these two fields. These cannot only be used to characterize and position neural-symbolic artificial intelligence approaches but also to identify a number of directions for further research.
AI algorithms that identify maneuvers from trajectory data could play an important role in improving flight safety and pilot training. AI challenges allow diverse teams to work together to solve hard problems and are an effective tool for developing AI solutions. AI challenges are also a key driver of AI computational requirements. The Maneuver Identification Challenge hosted at this http URL provides thousands of trajectories collected from pilots practicing in flight simulators, descriptions o...
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