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Multi-organ Segmentation over Partially Labeled Datasets with Multi-scale Feature Abstraction

,  - 2020

This paper presents a unified training strategy that enables a novel multi-scale deep neural network to be trained on multiple partially labeled datasets for multi-organ segmentation. Multi-scale contextual information is effective for pixel-level label prediction, i.e. image segmentation. However, such important information is only partially exp...


Attractive or Faithful? Popularity-Reinforced Learning for Inspired Headline Generation

, , , , ,  - 2020

With the rapid proliferation of online media sources and published news, headlines have become increasingly important for attracting readers to news articles, since users may be overwhelmed with the massive information. In this paper, we generate inspired headlines that preserve the nature of news articles and catch the eye of the reader simultan...


Plato Dialogue System: A Flexible Conversational AI Research Platform

, , , , ,  - 2020

As the field of Spoken Dialogue Systems and Conversational AI grows, so does the need for tools and environments that abstract away implementation details in order to expedite the development process, lower the barrier of entry to the field, and offer a common test-bed for new ideas. In this paper, we present Plato, a flexible Conversational AI p...


CAE-LO: LiDAR Odometry Leveraging Fully Unsupervised Convolutional Auto-Encoder for Interest Point Detection and Feature Description

, , , , , , , ,  - 2020

As an important technology in 3D mapping, autonomous driving, and robot navigation, LiDAR odometry is still a challenging task. Appropriate data structure and unsupervised deep learning are the keys to achieve an easy adjusted LiDAR odometry solution with high performance. Utilizing compact 2D structured spherical ring projection model and voxel ...


High-Fidelity Synthesis with Disentangled Representation

, , ,  - 2020

Learning disentangled representation of data without supervision is an important step towards improving the interpretability of generative models. Despite recent advances in disentangled representation learning, existing approaches often suffer from the trade-off between representation learning and generation performance i.e. improving generation...


Differential Evolution with Reversible Linear Transformations

, ,  - 2020

Differential evolution (DE) is a well-known type of evolutionary algorithms (EA). Similarly to other EA variants it can suffer from small populations and loose diversity too quickly. This paper presents a new approach to mitigate this issue: We propose to generate new candidate solutions by utilizing reversible linear transformation applied to a ...


A Knowledge-Enhanced Pretraining Model for Commonsense Story Generation

, , , ,  - 2020

Story generation, namely generating a reasonable story from a leading context, is an important but challenging task. In spite of the success in modeling fluency and local coherence, existing neural language generation models (e.g., GPT-2) still suffer from repetition, logic conflicts, and lack of long-range coherence in generated stories. We conj...


Knowledge-aware Attention Network for Protein-Protein Interaction Extraction

, , , , ,  - 2020

Protein-protein interaction (PPI) extraction from published scientific literature provides additional support for precision medicine efforts. However, many of the current PPI extraction methods need extensive feature engineering and cannot make full use of the prior knowledge in knowledge bases (KB). KBs contain huge amounts of structured informa...


A Differentiable Color Filter for Generating Unrestricted Adversarial Images

, ,  - 2020

We propose Adversarial Color Filtering (AdvCF), an approach that uses a differentiable color filter to create adversarial images. The color filter allows us to introduce large perturbations into images, while still maintaining or enhancing their photographic quality and appeal. AdvCF is motivated by properties that are necessary if adversarial im...


Listwise Learning to Rank by Exploring Unique Ratings

,  - 2020

In this paper, we propose new listwise learning-to-rank models that mitigate the shortcomings of existing ones. Existing listwise learning-to-rank models are generally derived from the classical Plackett-Luce model, which has three major limitations. (1) Its permutation probabilities overlook ties, i.e., a situation when more than one document ha...