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Richard M. Murray, Tung Phan-Minh, Sumanth Dathathri, Yuxiao Chen - 2020
There is a growing interest in building autonomous systems that interact with complex environments. The difficulty associated with obtaining an accurate model for such environments poses a challenge to the task of assessing and guaranteeing the system's performance. We present a data-driven solution that allows for a system to be evaluated for sp...
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-...
John P. Cunningham, Yansong Feng, Yao Fu - 2020
Paraphrase generation is a longstanding important problem in natural language processing. In addition, recent progress in deep generative models has shown promising results on discrete latent variables for text generation. Inspired by variational autoencoders with discrete latent structures, in this work, we propose a latent bag of words (BOW...
George Papamakarios, Iain Murray, Conor Durkan - 2020
Likelihood-free methods perform parameter inference in stochastic simulator models where evaluating the likelihood is intractable but sampling synthetic data is possible. One class of methods for this likelihood-free problem uses a classifier to distinguish between pairs of parameter-observation samples generated using the simulator and pairs sam...
Shai Avidan, Sharon Ayzik - 2020
We present a Deep Image Compression neural network that relies on side information, which is only available to the decoder. We base our algorithm on the assumption that the image available to the encoder and the image available to the decoder are correlated, and we let the network learn these correlations in the training phase. Then, at run tim...
Fatih Porikli, Ling Shao, Jianbing Shen, Chao Ma, Wenguan Wang, Xiankai Lu - 2020
We introduce a novel network, called CO-attention Siamese Network (COSNet), to address the unsupervised video object segmentation task from a holistic view. We emphasize the importance of inherent correlation among video frames and incorporate a global co-attention mechanism to improve further the state-of-the-art deep learning based solutions th...
Renato Renner, Hans J. Briegel, Henrik Wilming, Lea M. Trenkwalder, Sofiene Jerbi, Raban Iten, Tony Metger, Hendrik Poulsen Nautrup - 2020
To make progress in science, we often build abstract representations of physical systems that meaningfully encode information about the systems. The representations learnt by most current machine learning techniques reflect statistical structure present in the training data; however, these methods do not allow us to specify explicit and operation...
Minlie Huang, Xiaoyan Zhu, Zhihao Zhao, Fei Huang, Jian Guan - 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...
Pingkun Yan, Xi Fang - 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...
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...
