
StateRegularized Recurrent Neural Networks
Recurrent neural networks are a widely used class of neural architecture...
01/25/2019 ∙ by Cheng Wang, et al. ∙ 16 ∙ shareread it

Attending to Future Tokens For Bidirectional Sequence Generation
Neural sequence generation is typically performed tokenbytoken and lef...
08/16/2019 ∙ by Carolin Lawrence, et al. ∙ 3 ∙ shareread it

KBLRN : EndtoEnd Learning of Knowledge Base Representations with Latent, Relational, and Numerical Features
We present KBLRN, a novel framework for endtoend learning of knowledge...
09/14/2017 ∙ by Alberto GarcíaDurán, et al. ∙ 0 ∙ shareread it

Representation Learning for VisualRelational Knowledge Graphs
A visualrelational knowledge graph (KG) is a KG whose entities are asso...
09/07/2017 ∙ by Daniel OñoroRubio, et al. ∙ 0 ∙ shareread it

Lifted Probabilistic Inference for Asymmetric Graphical Models
Lifted probabilistic inference algorithms have been successfully applied...
12/01/2014 ∙ by Guy Van den Broeck, et al. ∙ 0 ∙ shareread it

Learning Convolutional Neural Networks for Graphs
Numerous important problems can be framed as learning from graph data. W...
05/17/2016 ∙ by Mathias Niepert, et al. ∙ 0 ∙ shareread it

Markov Chains on Orbits of Permutation Groups
We present a novel approach to detecting and utilizing symmetries in pro...
08/09/2014 ∙ by Mathias Niepert, et al. ∙ 0 ∙ shareread it

On the Conditional Independence Implication Problem: A LatticeTheoretic Approach
A latticetheoretic framework is introduced that permits the study of th...
08/09/2014 ∙ by Mathias Niepert, et al. ∙ 0 ∙ shareread it

Exchangeable Variable Models
A sequence of random variables is exchangeable if its joint distribution...
05/02/2014 ∙ by Mathias Niepert, et al. ∙ 0 ∙ shareread it

Tractability through Exchangeability: A New Perspective on Efficient Probabilistic Inference
Exchangeability is a central notion in statistics and probability theory...
01/07/2014 ∙ by Mathias Niepert, et al. ∙ 0 ∙ shareread it

RockIt: Exploiting Parallelism and Symmetry for MAP Inference in Statistical Relational Models
RockIt is a maximum aposteriori (MAP) query engine for statistical rela...
04/16/2013 ∙ by Jan Noessner, et al. ∙ 0 ∙ shareread it

SymmetryAware Marginal Density Estimation
The RaoBlackwell theorem is utilized to analyze and improve the scalabi...
04/09/2013 ∙ by Mathias Niepert, et al. ∙ 0 ∙ shareread it

Logical Inference Algorithms and Matrix Representations for Probabilistic Conditional Independence
Logical inference algorithms for conditional independence (CI) statement...
05/09/2012 ∙ by Mathias Niepert, et al. ∙ 0 ∙ shareread it

A Delayed Column Generation Strategy for Exact kBounded MAP Inference in Markov Logic Networks
The paper introduces kbounded MAP inference, a parameterization of MAP ...
03/15/2012 ∙ by Mathias Niepert, et al. ∙ 0 ∙ shareread it

TransRev: Modeling Reviews as Translations from Users to Items
The text of a review expresses the sentiment a customer has towards a pa...
01/30/2018 ∙ by Alberto GarcíaDurán, et al. ∙ 0 ∙ shareread it

Learning ShortCut Connections for Object Counting
Object counting is an important task in computer vision due to its growi...
05/08/2018 ∙ by Daniel OñoroRubio, et al. ∙ 0 ∙ shareread it

Contextual Hourglass Networks for Segmentation and Density Estimation
Hourglass networks such as the UNet and VNet are popular neural archit...
06/08/2018 ∙ by Daniel OñoroRubio, et al. ∙ 0 ∙ shareread it

On embeddings as alternative paradigm for relational learning
Many realworld domains can be expressed as graphs and, more generally, ...
06/29/2018 ∙ by Sebastijan Dumančić, et al. ∙ 0 ∙ shareread it

Towards a Spectrum of Graph Convolutional Networks
We present our ongoing work on understanding the limitations of graph co...
05/04/2018 ∙ by Mathias Niepert, et al. ∙ 0 ∙ shareread it

On embeddings as an alternative paradigm for relational learning
Many realworld domains can be expressed as graphs and, more generally, ...
06/29/2018 ∙ by Sebastijan Dumančić, et al. ∙ 0 ∙ shareread it

LRMM: Learning to Recommend with Missing Modalities
Multimodal learning has shown promising performance in contentbased rec...
08/21/2018 ∙ by Cheng Wang, et al. ∙ 0 ∙ shareread it

Learning Sequence Encoders for Temporal Knowledge Graph Completion
Research on link prediction in knowledge graphs has mainly focused on st...
09/10/2018 ∙ by Alberto GarcíaDurán, et al. ∙ 0 ∙ shareread it

Learning Representations of Missing Data for Predicting Patient Outcomes
Extracting actionable insight from Electronic Health Records (EHRs) pose...
11/12/2018 ∙ by Brandon Malone, et al. ∙ 0 ∙ shareread it

Learning Discrete Structures for Graph Neural Networks
Graph neural networks (GNNs) are a popular class of machine learning mod...
03/28/2019 ∙ by Luca Franceschi, et al. ∙ 0 ∙ shareread it

RecSysDAN: Discriminative Adversarial Networks for CrossDomain Recommender Systems
Data sparsity and data imbalance are practical and challenging issues in...
03/26/2019 ∙ by Cheng Wang, et al. ∙ 0 ∙ shareread it

MMKG: MultiModal Knowledge Graphs
We present MMKG, a collection of three knowledge graphs that contain bot...
03/13/2019 ∙ by Ye Liu, et al. ∙ 0 ∙ shareread it

Knowledge Graph Completion to Predict Polypharmacy Side Effects
The polypharmacy side effect prediction problem considers cases in which...
10/22/2018 ∙ by Brandon Malone, et al. ∙ 0 ∙ shareread it

BrainSlug: Transparent Acceleration of Deep Learning Through DepthFirst Parallelism
Neural network frameworks such as PyTorch and TensorFlow are the workhor...
04/23/2018 ∙ by Nicolas Weber, et al. ∙ 0 ∙ shareread it

Representation Learning for Resource Usage Prediction
Creating a model of a computer system that can be used for tasks such as...
02/02/2018 ∙ by Florian Schmidt, et al. ∙ 0 ∙ shareread it