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08/12/2023
Approximate Answering of Graph Queries
Knowledge graphs (KGs) are inherently incomplete because of incomplete w...
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07/26/2023
Efficient Learning of Discrete-Continuous Computation Graphs
Numerous models for supervised and reinforcement learning benefit from c...
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07/26/2023
Learning Disentangled Discrete Representations
Recent successes in image generation, model-based reinforcement learning...
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06/20/2023
LVM-Med: Learning Large-Scale Self-Supervised Vision Models for Medical Imaging via Second-order Graph Matching
Obtaining large pre-trained models that can be fine-tuned to new tasks w...
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05/17/2023
Tractable Probabilistic Graph Representation Learning with Graph-Induced Sum-Product Networks
We introduce Graph-Induced Sum-Product Networks (GSPNs), a new probabili...
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04/27/2023
Learning Neural PDE Solvers with Parameter-Guided Channel Attention
Scientific Machine Learning (SciML) is concerned with the development of...
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04/03/2023
Learning Sparsity of Representations with Discrete Latent Variables
Deep latent generative models have attracted increasing attention due to...
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12/10/2022
State-Regularized Recurrent Neural Networks to Extract Automata and Explain Predictions
Recurrent neural networks are a widely used class of neural architecture...
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10/17/2022
Joint Multilingual Knowledge Graph Completion and Alignment
Knowledge graph (KG) alignment and completion are usually treated as two...
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10/13/2022
PDEBENCH: An Extensive Benchmark for Scientific Machine Learning
Machine learning-based modeling of physical systems has experienced incr...
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10/04/2022
SIMPLE: A Gradient Estimator for k-Subset Sampling
k-subset sampling is ubiquitous in machine learning, enabling regulariza...
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09/28/2022
Learning to Explain Graph Neural Networks
Graph Neural Networks (GNNs) are a popular class of machine learning mod...
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09/11/2022
Adaptive Perturbation-Based Gradient Estimation for Discrete Latent Variable Models
The integration of discrete algorithmic components in deep learning arch...
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06/22/2022
Ordered Subgraph Aggregation Networks
Numerous subgraph-enhanced graph neural networks (GNNs) have emerged rec...
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10/15/2021
Integrating diverse extraction pathways using iterative predictions for Multilingual Open Information Extraction
In this paper we investigate a simple hypothesis for the Open Informatio...
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09/15/2021
AnnIE: An Annotation Platform for Constructing Complete Open Information Extraction Benchmark
Open Information Extraction (OIE) is the task of extracting facts from s...
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09/14/2021
BenchIE: Open Information Extraction Evaluation Based on Facts, Not Tokens
Intrinsic evaluations of OIE systems are carried out either manually – w...
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06/25/2021
VEGN: Variant Effect Prediction with Graph Neural Networks
Genetic mutations can cause disease by disrupting normal gene function. ...
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06/03/2021
Implicit MLE: Backpropagating Through Discrete Exponential Family Distributions
Integrating discrete probability distributions and combinatorial optimiz...
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11/24/2020
Uncertainty Estimation and Calibration with Finite-State Probabilistic RNNs
Uncertainty quantification is crucial for building reliable and trustabl...
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10/12/2020
Explaining Neural Matrix Factorization with Gradient Rollback
Explaining the predictions of neural black-box models is an important pr...
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04/06/2020
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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08/16/2019
Attending to Future Tokens For Bidirectional Sequence Generation
Neural sequence generation is typically performed token-by-token and lef...
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03/28/2019
Learning Discrete Structures for Graph Neural Networks
Graph neural networks (GNNs) are a popular class of machine learning mod...
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03/26/2019
RecSys-DAN: Discriminative Adversarial Networks for Cross-Domain Recommender Systems
Data sparsity and data imbalance are practical and challenging issues in...
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03/13/2019
MMKG: Multi-Modal Knowledge Graphs
We present MMKG, a collection of three knowledge graphs that contain bot...
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01/25/2019
State-Regularized Recurrent Neural Networks
Recurrent neural networks are a widely used class of neural architecture...
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11/12/2018
Learning Representations of Missing Data for Predicting Patient Outcomes
Extracting actionable insight from Electronic Health Records (EHRs) pose...
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10/22/2018
Knowledge Graph Completion to Predict Polypharmacy Side Effects
The polypharmacy side effect prediction problem considers cases in which...
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09/10/2018
Learning Sequence Encoders for Temporal Knowledge Graph Completion
Research on link prediction in knowledge graphs has mainly focused on st...
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08/21/2018
LRMM: Learning to Recommend with Missing Modalities
Multimodal learning has shown promising performance in content-based rec...
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06/29/2018
On embeddings as an alternative paradigm for relational learning
Many real-world domains can be expressed as graphs and, more generally, ...
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06/29/2018
On embeddings as alternative paradigm for relational learning
Many real-world domains can be expressed as graphs and, more generally, ...
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06/08/2018
Contextual Hourglass Networks for Segmentation and Density Estimation
Hourglass networks such as the U-Net and V-Net are popular neural archit...
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05/08/2018
Learning Short-Cut Connections for Object Counting
Object counting is an important task in computer vision due to its growi...
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05/04/2018
Towards a Spectrum of Graph Convolutional Networks
We present our ongoing work on understanding the limitations of graph co...
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04/23/2018
BrainSlug: Transparent Acceleration of Deep Learning Through Depth-First Parallelism
Neural network frameworks such as PyTorch and TensorFlow are the workhor...
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02/02/2018
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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01/30/2018
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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09/14/2017
KBLRN : End-to-End Learning of Knowledge Base Representations with Latent, Relational, and Numerical Features
We present KBLRN, a novel framework for end-to-end learning of knowledge...
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09/07/2017
Representation Learning for Visual-Relational Knowledge Graphs
A visual-relational knowledge graph (KG) is a KG whose entities are asso...
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05/17/2016
Learning Convolutional Neural Networks for Graphs
Numerous important problems can be framed as learning from graph data. W...
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12/01/2014
Lifted Probabilistic Inference for Asymmetric Graphical Models
Lifted probabilistic inference algorithms have been successfully applied...
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08/09/2014
Markov Chains on Orbits of Permutation Groups
We present a novel approach to detecting and utilizing symmetries in pro...
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08/09/2014
On the Conditional Independence Implication Problem: A Lattice-Theoretic Approach
A lattice-theoretic framework is introduced that permits the study of th...
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05/02/2014
Exchangeable Variable Models
A sequence of random variables is exchangeable if its joint distribution...
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01/07/2014
Tractability through Exchangeability: A New Perspective on Efficient Probabilistic Inference
Exchangeability is a central notion in statistics and probability theory...
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04/16/2013
RockIt: Exploiting Parallelism and Symmetry for MAP Inference in Statistical Relational Models
RockIt is a maximum a-posteriori (MAP) query engine for statistical rela...
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04/09/2013
Symmetry-Aware Marginal Density Estimation
The Rao-Blackwell theorem is utilized to analyze and improve the scalabi...
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05/09/2012