
Explain by Evidence: An Explainable Memorybased Neural Network for Question Answering
Interpretability and explainability of deep neural networks are challeng...
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Explain2Attack: Text Adversarial Attacks via CrossDomain Interpretability
Training robust deep learning models for downstream tasks is a critical...
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Towards Understanding Pixel Vulnerability under Adversarial Attacks for Images
Deep neural network image classifiers are reported to be susceptible to ...
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Effective Unsupervised Domain Adaptation with Adversarially Trained Language Models
Recent work has shown the importance of adaptation of broadcoverage con...
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QuatRE: RelationAware Quaternions for Knowledge Graph Embeddings
We propose a simple and effective embedding model, named QuatRE, to lear...
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Improving Ensemble Robustness by Collaboratively Promoting and Demoting Adversarial Robustness
Ensemblebased adversarial training is a principled approach to achieve ...
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Neural Sinkhorn Topic Model
In this paper, we present a new topic modelling approach via the theory ...
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Quaternion Graph Neural Networks
We consider reducing model parameters and moving beyond the Euclidean sp...
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MEDTEX: Transferring and Explaining Knowledge with Less Data from Pretrained Medical Imaging Models
Deep neural network based image classification methods usually require a...
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Improving Adversarial Robustness by Enforcing Local and Global Compactness
The fact that deep neural networks are susceptible to crafted perturbati...
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A SelfAttention Network based Node Embedding Model
Despite several signs of progress have been made recently, limited resea...
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OptiGAN: Generative Adversarial Networks for Goal Optimized Sequence Generation
One of the challenging problems in sequence generation tasks is the opti...
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A Capsule Networkbased Model for Learning Node Embeddings
In this paper, we focus on learning lowdimensional embeddings of entity...
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On Scalable Variant of Wasserstein Barycenter
We study a variant of Wasserstein barycenter problem, which we refer to ...
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Perturbations are not Enough: Generating Adversarial Examples with Spatial Distortions
Deep neural network image classifiers are reported to be susceptible to ...
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Unsupervised Universal SelfAttention Network for Graph Classification
Existing graph embedding models often have weaknesses in exploiting grap...
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Relational Memorybased Knowledge Graph Embedding
Knowledge graph embedding models often suffer from a limitation of remem...
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When Can Neural Networks Learn Connected Decision Regions?
Previous work has questioned the conditions under which the decision reg...
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Theoretical Perspective of Deep Domain Adaptation
Deep domain adaptation has recently undergone a big success. Compared wi...
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Probabilistic Multilevel Clustering via Composite Transportation Distance
We propose a novel probabilistic approach to multilevel clustering probl...
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A Capsule Networkbased Embedding Model for Knowledge Graph Completion and Search Personalization
In this paper, we introduce an embedding model, named CapsE, exploring a...
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Detection of Unknown Anomalies in Streaming Videos with Generative Energybased Boltzmann Models
Abnormal event detection is one of the important objectives in research ...
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A Capsule Networkbased Embedding Model for Search Personalization
Search personalization aims to tailor search results to each specific us...
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A Novel Embedding Model for Knowledge Base Completion Based on Convolutional Neural Network
We introduce a novel embedding method for knowledge base completion task...
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KGAN: How to Break The Minimax Game in GAN
Generative Adversarial Networks (GANs) were intuitively and attractively...
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Analogicalbased Bayesian Optimization
Some realworld problems revolve to solve the optimization problem _x∈Xf...
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Dual Discriminator Generative Adversarial Nets
We propose in this paper a novel approach to tackle the problem of mode ...
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Statistical Latent Space Approach for Mixed Data Modelling and Applications
The analysis of mixed data has been raising challenges in statistics and...
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Geometric Enclosing Networks
Training model to generate data has increasingly attracted research atte...
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MultiGenerator Generative Adversarial Nets
We propose a new approach to train the Generative Adversarial Nets (GANs...
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Multilevel Clustering via Wasserstein Means
We propose a novel approach to the problem of multilevel clustering, whi...
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A Random Finite Set Model for Data Clustering
The goal of data clustering is to partition data points into groups to m...
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ModelBased Multiple Instance Learning
While Multiple Instance (MI) data are point patterns  sets or multise...
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Clustering For Point Pattern Data
Clustering is one of the most common unsupervised learning tasks in mach...
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Modelbased Classification and Novelty Detection For Point Pattern Data
Point patterns are sets or multisets of unordered elements that can be ...
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Stabilizing Linear Prediction Models using Autoencoder
To date, the instability of prognostic predictors in a sparse high dimen...
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Column Networks for Collective Classification
Relational learning deals with data that are characterized by relational...
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Outlier Detection on MixedType Data: An Energybased Approach
Outlier detection amounts to finding data points that differ significant...
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Faster Training of Very Deep Networks Via pNorm Gates
A major contributing factor to the recent advances in deep neural networ...
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Preterm Birth Prediction: Deriving Stable and Interpretable Rules from High Dimensional Data
Preterm births occur at an alarming rate of 1015 risk of infant mortali...
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An evaluation of randomized machine learning methods for redundant data: Predicting short and mediumterm suicide risk from administrative records and risk assessments
Accurate prediction of suicide risk in mental health patients remains an...
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Approximation Vector Machines for Largescale Online Learning
One of the most challenging problems in kernel online learning is to bou...
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Learning deep representation of multityped objects and tasks
We introduce a deep multitask architecture to integrate multityped repre...
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Choice by Elimination via Deep Neural Networks
We introduce Neural Choice by Elimination, a new framework that integrat...
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Collaborative filtering via sparse Markov random fields
Recommender systems play a central role in providing individualized acce...
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DeepCare: A Deep Dynamic Memory Model for Predictive Medicine
Personalized predictive medicine necessitates the modeling of patient il...
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Hierarchical Dirichlet process for tracking complex topical structure evolution and its application to autism research literature
In this paper we describe a novel framework for the discovery of the top...
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MCMC for Hierarchical SemiMarkov Conditional Random Fields
Deep architecture such as hierarchical semiMarkov models is an importan...
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MixedVariate Restricted Boltzmann Machines
Modern datasets are becoming heterogeneous. To this end, we present in t...
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Thurstonian Boltzmann Machines: Learning from Multiple Inequalities
We introduce Thurstonian Boltzmann Machines (TBM), a unified architectur...
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