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Monica S. Lam, Jian Li, Giovanni Campagna, Silei Xu - 2020
Virtual assistants have proprietary third-party skill platforms; they train and own the voice interface to websites based on their submitted skill information. This paper proposes Schema2QA, an open-source toolkit that leverages the Schema.org markup found in many websites to automatically build skills. Schema2QA has several advantages: (1) Schem...
Janis Keuper, Nicolas R. Gauger, Franz-Josef Pfreundt, Sabine Müller, Raju Ram - 2020
Most machine learning methods require careful selection of hyper-parameters in order to train a high performing model with good generalization abilities. Hence, several automatic selection algorithms have been introduced to overcome tedious manual (try and error) tuning of these parameters. Due to its very high sample efficiency, Bayesian Optimiz...
Kiho Hong, Taeryun Won, Jungkyu Lee - 2020
Recent studies in image classification have demonstrated a variety of techniques for improving the performance of Convolutional Neural Networks (CNNs). However, attempts to combine existing techniques to create a practical model are still uncommon. In this study, we carry out extensive experiments to validate that carefully assembling these techn...
Guilherme Maeda, Prabhat Nagarajan, Zhang-Wei Hong - 2020
Off-policy ensemble reinforcement learning (RL) methods have demonstrated impressive results across a range of RL benchmark tasks. Recent works suggest that directly imitating experts' policies in a supervised manner before or during the course of training enables faster policy improvement for an RL agent. Motivated by these recent insights, we p...
Jiawei Zhang - 2020
In graph instance representation learning, both the diverse graph instance sizes and the graph node orderless property have been the major obstacles that render existing representation learning models fail to work. In this paper, we will examine the effectiveness of GRAPH-BERT on graph instance representation learning, which was designed for node...
Hugo Proença, Aruna Kumar, Diana Borza, Ehsan Yaghoubi - 2020
Attention is defined as the preparedness for the mental selection of certain aspects in a physical environment. In the computer vision domain, this mechanism is of most interest, as it helps to define the segments of an image/video that are critical for obtaining a specific decision. This paper introduces one 'implicit' attentional mechanism for ...
Vadim Turlapov, Elena Shipunova, Dmitrii Lachinov - 2020
The segmentation of brain tumors in multimodal MRIs is one of the most challenging tasks in medical image analysis. The recent state of the art algorithms solving this task is based on machine learning approaches and deep learning in particular. The amount of data used for training such models and its variability is a keystone for building an alg...
Noah A. Smith, Dallas Card - 2020
Recent work on fairness in machine learning has primarily emphasized how to define, quantify, and encourage "fair" outcomes. Less attention has been paid, however, to the ethical foundations which underlie such efforts. Among the ethical perspectives that should be taken into consideration is consequentialism, the position that, roughly speaking,...
Julien Velcin, Adrien Guille, Robin Brochier - 2020
Document network embedding aims at learning representations for a structured text corpus i.e. when documents are linked to each other. Recent algorithms extend network embedding approaches by incorporating the text content associated with the nodes in their formulations. In most cases, it is hard to interpret the learned representations. Moreover...
Jimeng Sun, Duen Horng Chau, Cao Xiao, Scott Freitas, Rahul Duggal - 2020
In recent years, significant attention has been devoted towards integrating deep learning technologies in the healthcare domain. However, to safely and practically deploy deep learning models for home health monitoring, two significant challenges must be addressed: the models should be (1) robust against noise; and (2) compact and energy-efficien...
