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Youliang Yan, Yongming Huang, Chunhua Shen, Zhi Tian, Kunyang Sun, Hao Chen - 2020
Instance segmentation is one of the fundamental vision tasks. Recently, fully convolutional instance segmentation methods have drawn much attention as they are often simpler and more efficient than two-stage approaches like Mask R-CNN. To date, almost all such approaches fall behind the two-stage Mask R-CNN method in mask precision when models ha...
Margarida Carvalho, Mostafa ElAraby, Guy Wolf - 2020
We introduce a novel approach to optimize the architecture of deep neural networks by identifying critical neurons and removing non-critical ones. The proposed approach utilizes a mixed integer programming (MIP) formulation of neural models which includes a continuous importance score computed for each neuron in the network. The optimization in MIP...
Kobi Cohen, Dor Livne - 2020
The recent success of deep neural networks (DNNs) for function approximation in reinforcement learning has triggered the development of Deep Reinforcement Learning (DRL) algorithms in various fields, such as robotics, computer games, natural language processing, computer vision, sensing systems, and wireless networking. Unfortunately, DNNs suffer...
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...
Matthias Zwicker, L. Andrea Dunbar, Tiziano Portenier, Geng Lin, Siavash A. Bigdeli - 2020
Learning generative probabilistic models that can estimate the continuous density given a set of samples, and that can sample from that density, is one of the fundamental challenges in unsupervised machine learning. In this paper we introduce a new approach to obtain such models based on what we call denoising density estimators (DDEs). A DDE is ...
Alexander Löser, Felix A. Gers, Paul Grundmann, Betty van Aken, Sebastian Arnold - 2020
We present Contextual Discourse Vectors (CDV), a distributed document representation for efficient answer retrieval from long healthcare documents. Our approach is based on structured query tuples of entities and aspects from free text and medical taxonomies. Our model leverages a dual encoder architecture with hierarchical LSTM layers and multi-...
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...
Jie Zhou, Cheng Niu, Yang Feng, Jinchao Zhang, Zongjia Li, Zekang Li - 2020
Audio-Visual Scene-Aware Dialog (AVSD) is a task to generate responses when chatting about a given video, which is organized as a track of the 8th Dialog System Technology Challenge (DSTC8). To solve the task, we propose a universal multimodal transformer and introduce the multi-task learning method to learn joint representations among different ...
Abdolah Chalechale, Pegah Salehi - 2020
The diagnosis of cancer is mainly performed by visual analysis of the pathologists, through examining the morphology of the tissue slices and the spatial arrangement of the cells. If the microscopic image of a specimen is not stained, it will look colorless and textured. Therefore, chemical staining is required to create contrast and help identif...
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...
