Articles by aver
Neural Concept Formation in Knowledge Graphs
I am on the job market!
Dagsthul here we come!
PC Tutorial @ ECML-PKDD2020
Probabilistic Inference with Algebraic Constraints: Theoretical Limits and Practical Approximations
Approximate WMI @ NeurIPS2020
RAEs @ COLING2020
Probabilistic Circuits: Inference, Representation, Learning and Applications
PC Tutorial @ ECAI2020
From Variational to Deterministic Autoencoders. Or the Joys of Density Estimation in Latent Spaces
RAEs @ UCL
Imagining Grounded Conceptual Representations from Perceptual Information in Situated Guessing Games
PCs @ PGM2020
Handling Missing Data in Decision Trees: A Probabilistic Approach
EiNets + MP-WMI@ICML2020
Einsum Networks: Fast and Scalable Learning of Tractable Probabilistic Circuits
Scaling up Hybrid Probabilistic Inference with Logical and Arithmetic Constraints via Message Passing
Conditional Sum-Product Networks: Imposing Structure on Deep Probabilistic Architectures
Strudel: Learning Structured-Decomposable Probabilistic Circuits
From Variational to Deterministic Autoencoders
quarentine
visiting PL@MPI
Tractable probabilistic inference with probabilistic circuits
Probabilistic circuits: a tutorial
pc tutorial@AAAI20
Tractable Probabilistic Modeling Meeting
RAE@ICLR 2020
t prime@NeurIPS 2019
pc tutorial@Stanford
Probabilistic circuits: a tutorial
MP-MI@KR2ML@NeurIPS19
Hybrid Probabilistic Inference with Logical Constraints: Tractability and Message-Passing
On Tractable Computation of Expected Predictions
Automatic Bayesian density analysis
Random Sum-Product Networks: A Simple and Effective Approach to Probabilistic Deep Learning
Visualizing and understanding Sum-Product Networks
Ensembles of density estimators for positive-unlabeled learning
Expected predictions@NeurIPS19
tutorial@UAI19
organizing TPM 2019
Tractable probabilistic models
RAT-SPNs@UAI 2019
new adventures at UCLA
Sum-product autoencoding: Encoding and decoding representations using sum-product networks
Mixed sum-product networks: A deep architecture for hybrid domains
Bayesian Nonparametric Hawkes Processes
Sum-Product Network structure learning by efficient product nodes discovery
Fast and Accurate Density Estimation with Extremely Randomized Cutset Networks
Encoding and Decoding Representations with Sum- and Max-Product Networks
End-to-end Learning of Deep Spatio-temporal Representations for Satellite Image Time Series Classification
Alternative Variable Splitting Methods to Learn Sum-Product Networks
Generative Probabilistic Models for Positive-Unlabeled Learning
Learning Sum-Product Networks
Towards Representation Learning with Tractable Probabilistic Models
Multi-Label Classification with Cutset Networks
Learning Bayesian Random Cutset Forests
Learning Accurate Cutset Networks by Exploiting Decomposability
Simplifying, Regularizing and Strengthening Sum-Product Network Structure Learning
Isabel Valera
Brief summary
Antonio Vergari
Some description