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05/31/2023
Knowledge Graph Embeddings in the Biomedical Domain: Are They Useful? A Look at Link Prediction, Rule Learning, and Downstream Polypharmacy Tasks
Knowledge graphs are powerful tools for representing and organising comp...
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05/31/2023
Not All Neuro-Symbolic Concepts Are Created Equal: Analysis and Mitigation of Reasoning Shortcuts
Neuro-Symbolic (NeSy) predictive models hold the promise of improved com...
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05/25/2023
How to Turn Your Knowledge Graph Embeddings into Generative Models via Probabilistic Circuits
Some of the most successful knowledge graph embedding (KGE) models for l...
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03/16/2023
From MNIST to ImageNet and Back: Benchmarking Continual Curriculum Learning
Continual learning (CL) is one of the most promising trends in recent ma...
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10/05/2022
ChemAlgebra: Algebraic Reasoning on Chemical Reactions
While showing impressive performance on various kinds of learning tasks,...
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06/01/2022
Semantic Probabilistic Layers for Neuro-Symbolic Learning
We design a predictive layer for structured-output prediction (SOP) that...
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02/17/2022
Efficient and Reliable Probabilistic Interactive Learning with Structured Outputs
In this position paper, we study interactive learning for structured out...
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02/21/2021
Tractable Computation of Expected Kernels by Circuits
Computing the expectation of some kernel function is ubiquitous in machi...
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02/11/2021
A Compositional Atlas of Tractable Circuit Operations: From Simple Transformations to Complex Information-Theoretic Queries
Circuit representations are becoming the lingua franca to express and re...
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01/31/2021
An Empirical Study on the Generalization Power of Neural Representations Learned via Visual Guessing Games
Guessing games are a prototypical instance of the "learning by interacti...
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11/05/2020
Imagining Grounded Conceptual Representations from Perceptual Information in Situated Guessing Games
In visual guessing games, a Guesser has to identify a target object in a...
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07/18/2020
Strudel: Learning Structured-Decomposable Probabilistic Circuits
Probabilistic circuits (PCs) represent a probability distribution as a c...
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06/29/2020
Handling Missing Data in Decision Trees: A Probabilistic Approach
Decision trees are a popular family of models due to their attractive pr...
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04/13/2020
Einsum Networks: Fast and Scalable Learning of Tractable Probabilistic Circuits
Probabilistic circuits (PCs) are a promising avenue for probabilistic mo...
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02/28/2020
Scaling up Hybrid Probabilistic Inference with Logical and Arithmetic Constraints via Message Passing
Weighted model integration (WMI) is a very appealing framework for proba...
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10/05/2019
On Tractable Computation of Expected Predictions
Computing expected predictions has many interesting applications in area...
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09/20/2019
Hybrid Probabilistic Inference with Logical Constraints: Tractability and Message-Passing
Weighted model integration (WMI) is a very appealing framework for proba...
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05/21/2019
Conditional Sum-Product Networks: Imposing Structure on Deep Probabilistic Architectures
Bayesian networks are a central tool in machine learning and artificial ...
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03/29/2019
From Variational to Deterministic Autoencoders
Variational Autoencoders (VAEs) provide a theoretically-backed framework...
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01/11/2019
SPFlow: An Easy and Extensible Library for Deep Probabilistic Learning using Sum-Product Networks
We introduce SPFlow, an open-source Python library providing a simple in...
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07/24/2018
Automatic Bayesian Density Analysis
Making sense of a dataset in an automatic and unsupervised fashion is a ...
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06/05/2018
Probabilistic Deep Learning using Random Sum-Product Networks
Probabilistic deep learning currently receives an increased interest, as...
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10/09/2017
Sum-Product Networks for Hybrid Domains
While all kinds of mixed data -from personal data, over panel and scient...
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08/29/2016
Visualizing and Understanding Sum-Product Networks
Sum-Product Networks (SPNs) are recently introduced deep tractable proba...
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08/08/2016
