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1. GraphMemDialog: Optimizing End-to-End Task-Oriented Dialog Systems Using Graph Memory Networks

   https://doi.org/10.1609/aaai.v36i10.21403  

   Wu, Jie ; Harris, Ian G ; Zhao, Hongzhi ( June 2022 , Proceedings of the AAAI Conference on Artificial Intelligence)

   Effectively integrating knowledge into end-to-end task-oriented dialog systems remains a challenge. It typically requires incorporation of an external knowledge base (KB) and capture of the intrinsic semantics of the dialog history. Recent research shows promising results by using Sequence-to-Sequence models, Memory Networks, and even Graph Convolutional Networks. However, current state-of-the-art models are less effective at integrating dialog history and KB into task-oriented dialog systems in the following ways: 1. The KB representation is not fully context-aware. The dynamic interaction between the dialog history and KB is seldom explored. 2. Both the sequential and structural information in the dialog history can contribute to capturing the dialog semantics, but they are not studied concurrently. In this paper, we propose a novel Graph Memory Network (GMN) based Seq2Seq model, GraphMemDialog, to effectively learn the inherent structural information hidden in dialog history, and to model the dynamic interaction between dialog history and KBs. We adopt a modified graph attention network to learn the rich structural representation of the dialog history, whereas the context-aware representation of KB entities are learnt by our novel GMN. To fully exploit this dynamic interaction, we design a learnable memory controller coupled with external KB entity memories to recurrently incorporatemore »dialog history context into KB entities through a multi-hop reasoning mechanism. Experiments on three public datasets show that our GraphMemDialog model achieves state-of-the-art performance and outperforms strong baselines by a large margin, especially on datatests with more complicated KB information.« less
2. Graph Clustering with Embedding Propagation

   https://doi.org/10.1109/BigData50022.2020.9378031  

   Yang, Carl ; Liu, Liyuan ; Liu, Mengxiong ; Wang, Zongyi ; Zhang, Chao ; Han, Jiawei ( December 2020 , BigData'20: IEEE 2020 Int. Conf. on Big Data, Dec. 2020)

   In the past decade, the amount of attributed network data has skyrocketed, and the problem of identifying their underlying group structures has received significant attention. By leveraging both attribute and link information, recent state-of-the-art network clustering methods have achieved significant improvements on relatively clean datasets. However, the noisy nature of real-world attributed networks has long been overlooked, which leads to degraded performance facing missing or inaccurate attributes and links. In this work, we overcome such weaknesses by marrying the strengths of clustering and embedding on attributed networks. Specifically, we propose GRACE (GRAph Clustering with Embedding propagation), to simultaneously learn network representations and identify network clusters in an end-to-end manner. It employs deep denoise autoencoders to generate robust network embeddings from node attributes, propagates the embeddings in the network to capture node interactions, and detects clusters based on the stable state of embedding propagation. To provide more insight, we further analyze GRACE in a theoretical manner and find its underlying connections with two canonical approaches for network modeling. Extensive experiments on six real-world attributed networks demonstrate the superiority of GRACE over various baselines from the state-of-the-art. Remarkably, GRACE improves the averaged performance of the strongest baseline from 0.43 to 0.52, yieldingmore »a 21% relative improvement. Controlled experiments and case studies further verify our intuitions and demonstrate the ability of GRACE to handle noisy information in real-world attributed networks.« less
3. BNU-Net: A Novel Deep Learning Approach for LV MRI Analysis in Short-Axis MRI

   https://doi.org/10.1109/BIBE.2019.00137  

   Chu, Wenhui ; Molina, Giovanni ; Navkar, Nikhil V. ; Eick, Christoph F. ; Becker, Aaron T. ; Tsiamyrtzis, Panagiotis ; Tsekos, Nikolaos V. ( October 2019 , 2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE))

   This work presents a novel deep learning architecture called BNU-Net for the purpose of cardiac segmentation based on short-axis MRI images. Its name is derived from the Batch Normalized (BN) U-Net architecture for medical image segmentation. New generations of deep neural networks (NN) are called convolutional NN (CNN). CNNs like U-Net have been widely used for image classification tasks. CNNs are supervised training models which are trained to learn hierarchies of features automatically and robustly perform classification. Our architecture consists of an encoding path for feature extraction and a decoding path that enables precise localization. We compare this approach with a parallel approach named U-Net. Both BNU-Net and U-Net are cardiac segmentation approaches: while BNU-Net employs batch normalization to the results of each convolutional layer and applies an exponential linear unit (ELU) approach that operates as activation function, U-Net does not apply batch normalization and is based on Rectified Linear Units (ReLU). The presented work (i) facilitates various image preprocessing techniques, which includes affine transformations and elastic deformations, and (ii) segments the preprocessed images using the new deep learning architecture. We evaluate our approach on a dataset containing 805 MRI images from 45 patients. The experimental results reveal that ourmore »approach accomplishes comparable or better performance than other state-of-the-art approaches in terms of the Dice coefficient and the average perpendicular distance.« less
4. BNU- Net: a Novel Deep Learning Approach for LV MRI Analysis in Short-Axis MRI

   Chu, Wenhui ; Molina, Giovanni ; Navkar, Nikhil ; Eick, Christoph ; Becker, Aaron ; Tsekos, Nikolaos ( October 2019 , IEEE International Conference on Bioinformatics and Bioengineering (BIBE))

   This work presents a novel deep learning architecture called BNU-Net for the purpose of cardiac segmentation based on short-axis MRI images. Its name is derived from the Batch Normalized (BN) U-Net architecture for medical image segmentation. New generations of deep neural networks (NN) are called convolutional NN (CNN). CNNs like U-Net have been widely used for image classification tasks. CNNs are supervised training models which are trained to learn hierarchies of features automatically and robustly perform classification. Our architecture consists of an encoding path for feature extraction and a decoding path that enables precise localization. We compare this approach with a parallel approach named U-Net. Both BNU-Net and U-Net are cardiac segmentation approaches: while BNU-Net employs batch normalization to the results of each convolutional layer and applies an exponential linear unit (ELU) approach that operates as activation function, U-Net does not apply batch normalization and is based on Rectified Linear Units (ReLU). The presented work (i) facilitates various image preprocessing techniques, which includes affine transformations and elastic deformations, and (ii) segments the preprocessed images using the new deep learning architecture. We evaluate our approach on a dataset containing 805 MRI images from 45 patients. The experimental results reveal that ourmore »approach accomplishes comparable or better performance than other state-of-the-art approaches in terms of the Dice coefficient and the average perpendicular distance. Index Terms—Magnetic Resonance Imaging; Batch Normalization; Exponential Linear Units« less
5. A framework for machine learning of model error in dynamical systems

   https://doi.org/10.1090/cams/10  

   Levine, Matthew ; Stuart, Andrew ( October 2022 , Communications of the American Mathematical Society)

   The development of data-informed predictive models for dynamical systems is of widespread interest in many disciplines. We present a unifying framework for blending mechanistic and machine-learning approaches to identify dynamical systems from noisily and partially observed data. We compare pure data-driven learning with hybrid models which incorporate imperfect domain knowledge, referring to the discrepancy between an assumed truth model and the imperfect mechanistic model as model error. Our formulation is agnostic to the chosen machine learning model, is presented in both continuous- and discrete-time settings, and is compatible both with model errors that exhibit substantial memory and errors that are memoryless. First, we study memoryless linear (w.r.t. parametric-dependence) model error from a learning theory perspective, defining excess risk and generalization error. For ergodic continuous-time systems, we prove that both excess risk and generalization error are bounded above by terms that diminish with the square-root of T T , the time-interval over which training data is specified. Secondly, we study scenarios that benefit from modeling with memory, proving universal approximation theorems for two classes of continuous-time recurrent neural networks (RNNs): both can learn memory-dependent model error, assuming that it is governed by a finite-dimensional hidden variable and that, together, the observedmore »and hidden variables form a continuous-time Markovian system. In addition, we connect one class of RNNs to reservoir computing, thereby relating learning of memory-dependent error to recent work on supervised learning between Banach spaces using random features. Numerical results are presented (Lorenz ’63, Lorenz ’96 Multiscale systems) to compare purely data-driven and hybrid approaches, finding hybrid methods less datahungry and more parametrically efficient. We also find that, while a continuous-time framing allows for robustness to irregular sampling and desirable domain- interpretability, a discrete-time framing can provide similar or better predictive performance, especially when data are undersampled and the vector field defining the true dynamics cannot be identified. Finally, we demonstrate numerically how data assimilation can be leveraged to learn hidden dynamics from noisy, partially-observed data, and illustrate challenges in representing memory by this approach, and in the training of such models.« less