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BiOnt: Deep Learning using Multiple Biomedical Ontologies for Relation Extraction

Francisco M. Couto, Diana Sousa  - 2020

Successful biomedical relation extraction can provide evidence to researchers and clinicians about possible unknown associations between biomedical entities, advancing the current knowledge we have about those entities and their inherent mechanisms. Most biomedical relation extraction systems do not resort to external sources of knowledge, such a...

AnomalyDAE: Dual autoencoder for anomaly detection on attributed networks

Zuoyong Li, Fengbin Zhang, Haoyi Fan  - 2020

Anomaly detection on attributed networks aims at finding nodes whose patterns deviate significantly from the majority of reference nodes, which is pervasive in many applications such as network intrusion detection and social spammer detection. However, most existing methods neglect the complex cross-modality interactions between network structure...

Zero-Shot Video Object Segmentation via Attentive Graph Neural Networks

Ling Shao, David Crandall, Jianbing Shen, Xiankai Lu, Wenguan Wang  - 2020

This work proposes a novel attentive graph neural network (AGNN) for zero-shot video object segmentation (ZVOS). The suggested AGNN recasts this task as a process of iterative information fusion over video graphs. Specifically, AGNN builds a fully connected graph to efficiently represent frames as nodes, and relations between arbitrary frame pair...

Simulation of electron-proton scattering events by a Feature-Augmented and Transformed Generative Adversarial Network (FAT-GAN)

Yaohang Li, Luisa Velasco, Ryan Strauss, Michael Robertson, Evan Pritchard, Michelle P. Kuchera, W. Melnitchouk, Tianbo Liu, N. Sato, Yasir Alanazi  - 2020

We apply generative adversarial network (GAN) technology to build an event generator that simulates particle production in electron-proton scattering that is free of theoretical assumptions about underlying particle dynamics. The difficulty of efficiently training a GAN event simulator lies in learning the complicated patterns of the distribution...

Objective Social Choice: Using Auxiliary Information to Improve Voting Outcomes

Michael R. Zhang, Silviu Pitis  - 2020

How should one combine noisy information from diverse sources to make an inference about an objective ground truth? This frequently recurring, normative question lies at the core of statistics, machine learning, policy-making, and everyday life. It has been called "combining forecasts", "meta-analysis", "ensembling", and the "MLE approach to voti...

Joint Geographical and Temporal Modeling based on Matrix Factorization for Point-of-Interest Recommendation

Fabio Crestani, Mitra Baratchi, Mohammad Aliannejadi, Hossein A. Rahmani  - 2020

With the popularity of Location-based Social Networks, Point-of-Interest (POI) recommendation has become an important task, which learns the users' preferences and mobility patterns to recommend POIs. Previous studies show that incorporating contextual information such as geographical and temporal influences is necessary to improve POI recommenda...

The troublesome kernel: why deep learning for inverse problems is typically unstable

Anders C. Hansen, Ben Adcock, Vegard Antun, Nina M. Gottschling  - 2020

There is overwhelming empirical evidence that Deep Learning (DL) leads to unstable methods in applications ranging from image classification and computer vision to voice recognition and automated diagnosis in medicine. Recently, a similar instability phenomenon has been discovered when DL is used to solve certain problems in computational science...

Lesion Harvester: Iteratively Mining Unlabeled Lesions and Hard-Negative Examples at Scale

Le Lu, Lin Yang, Jing Xiao, Yuankai Huo, Ke Yan, Youjing Zheng, Adam P. Harrison, Jinzheng Cai  - 2020

Acquiring large-scale medical image data, necessary for training machine learning algorithms, is frequently intractable, due to prohibitive expert-driven annotation costs. Recent datasets extracted from hospital archives, e.g., DeepLesion, have begun to address this problem. However, these are often incompletely or noisily labeled, e.g., DeepLesi...

Unpaired Multi-modal Segmentation via Knowledge Distillation

Ben Glocker, Pheng Ann Heng, Quande Liu, Qi Dou  - 2020

Multi-modal learning is typically performed with network architectures containing modality-specific layers and shared layers, utilizing co-registered images of different modalities. We propose a novel learning scheme for unpaired cross-modality image segmentation, with a highly compact architecture achieving superior segmentation accuracy. In our...

Graph Constrained Reinforcement Learning for Natural Language Action Spaces

Matthew Hausknecht, Prithviraj Ammanabrolu  - 2020

Interactive Fiction games are text-based simulations in which an agent interacts with the world purely through natural language. They are ideal environments for studying how to extend reinforcement learning agents to meet the challenges of natural language understanding, partial observability, and action generation in combinatorially-large text-b...