
VEGN: Variant Effect Prediction with Graph Neural Networks
Genetic mutations can cause disease by disrupting normal gene function. ...
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Implicit MLE: Backpropagating Through Discrete Exponential Family Distributions
Integrating discrete probability distributions and combinatorial optimiz...
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Uncertainty Estimation and Calibration with FiniteState Probabilistic RNNs
Uncertainty quantification is crucial for building reliable and trustabl...
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Explaining Neural Matrix Factorization with Gradient Rollback
Explaining the predictions of neural blackbox models is an important pr...
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Answering Complex Queries in Knowledge Graphs with Bidirectional Sequence Encoders
Representation learning for knowledge graphs (KGs) has focused on the pr...
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Attending to Future Tokens For Bidirectional Sequence Generation
Neural sequence generation is typically performed tokenbytoken and lef...
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Learning Discrete Structures for Graph Neural Networks
Graph neural networks (GNNs) are a popular class of machine learning mod...
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RecSysDAN: Discriminative Adversarial Networks for CrossDomain Recommender Systems
Data sparsity and data imbalance are practical and challenging issues in...
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MMKG: MultiModal Knowledge Graphs
We present MMKG, a collection of three knowledge graphs that contain bot...
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StateRegularized Recurrent Neural Networks
Recurrent neural networks are a widely used class of neural architecture...
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Learning Representations of Missing Data for Predicting Patient Outcomes
Extracting actionable insight from Electronic Health Records (EHRs) pose...
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Knowledge Graph Completion to Predict Polypharmacy Side Effects
The polypharmacy side effect prediction problem considers cases in which...
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Learning Sequence Encoders for Temporal Knowledge Graph Completion
Research on link prediction in knowledge graphs has mainly focused on st...
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LRMM: Learning to Recommend with Missing Modalities
Multimodal learning has shown promising performance in contentbased rec...
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On embeddings as an alternative paradigm for relational learning
Many realworld domains can be expressed as graphs and, more generally, ...
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On embeddings as alternative paradigm for relational learning
Many realworld domains can be expressed as graphs and, more generally, ...
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Contextual Hourglass Networks for Segmentation and Density Estimation
Hourglass networks such as the UNet and VNet are popular neural archit...
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Learning ShortCut Connections for Object Counting
Object counting is an important task in computer vision due to its growi...
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Towards a Spectrum of Graph Convolutional Networks
We present our ongoing work on understanding the limitations of graph co...
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BrainSlug: Transparent Acceleration of Deep Learning Through DepthFirst Parallelism
Neural network frameworks such as PyTorch and TensorFlow are the workhor...
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Representation Learning for Resource Usage Prediction
Creating a model of a computer system that can be used for tasks such as...
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TransRev: Modeling Reviews as Translations from Users to Items
The text of a review expresses the sentiment a customer has towards a pa...
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KBLRN : EndtoEnd Learning of Knowledge Base Representations with Latent, Relational, and Numerical Features
We present KBLRN, a novel framework for endtoend learning of knowledge...
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Representation Learning for VisualRelational Knowledge Graphs
A visualrelational knowledge graph (KG) is a KG whose entities are asso...
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Learning Convolutional Neural Networks for Graphs
Numerous important problems can be framed as learning from graph data. W...
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Lifted Probabilistic Inference for Asymmetric Graphical Models
Lifted probabilistic inference algorithms have been successfully applied...
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Markov Chains on Orbits of Permutation Groups
We present a novel approach to detecting and utilizing symmetries in pro...
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On the Conditional Independence Implication Problem: A LatticeTheoretic Approach
A latticetheoretic framework is introduced that permits the study of th...
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Exchangeable Variable Models
A sequence of random variables is exchangeable if its joint distribution...
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Tractability through Exchangeability: A New Perspective on Efficient Probabilistic Inference
Exchangeability is a central notion in statistics and probability theory...
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RockIt: Exploiting Parallelism and Symmetry for MAP Inference in Statistical Relational Models
RockIt is a maximum aposteriori (MAP) query engine for statistical rela...
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SymmetryAware Marginal Density Estimation
The RaoBlackwell theorem is utilized to analyze and improve the scalabi...
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Logical Inference Algorithms and Matrix Representations for Probabilistic Conditional Independence
Logical inference algorithms for conditional independence (CI) statement...
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A Delayed Column Generation Strategy for Exact kBounded MAP Inference in Markov Logic Networks
The paper introduces kbounded MAP inference, a parameterization of MAP ...
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