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Solving Mixed Integer Programs Using Neural Networks
Mixed Integer Programming (MIP) solvers rely on an array of sophisticate...
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Deep Bayesian Bandits: Exploring in Online Personalized Recommendations
Recommender systems trained in a continuous learning fashion are plagued...
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Model Size Reduction Using Frequency Based Double Hashing for Recommender Systems
Deep Neural Networks (DNNs) with sparse input features have been widely ...
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Privacy-Preserving Recommender Systems Challenge on Twitter's Home Timeline
Recommender systems constitute the core engine of most social network pl...
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Addressing Delayed Feedback for Continuous Training with Neural Networks in CTR prediction
One of the challenges in display advertising is that the distribution of...
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Graph Saliency Maps through Spectral Convolutional Networks: Application to Sex Classification with Brain Connectivity
Graph convolutional networks (GCNs) allow to apply traditional convoluti...
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Disease Prediction using Graph Convolutional Networks: Application to Autism Spectrum Disorder and Alzheimer's Disease
Graphs are widely used as a natural framework that captures interactions...
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DLTK: State of the Art Reference Implementations for Deep Learning on Medical Images
We present DLTK, a toolkit providing baseline implementations for effici...
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Exploring Heritability of Functional Brain Networks with Inexact Graph Matching
Data-driven brain parcellations aim to provide a more accurate represent...
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Spectral Graph Convolutions for Population-based Disease Prediction
Exploiting the wealth of imaging and non-imaging information for disease...
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Distance Metric Learning using Graph Convolutional Networks: Application to Functional Brain Networks
Evaluating similarity between graphs is of major importance in several c...
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Comparison of Brain Networks with Unknown Correspondences
Graph theory has drawn a lot of attention in the field of Neuroscience d...
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