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Found 35 papers, 17 papers with code
Towards Representation Learning with Tractable Probabilistic Models
Probabilistic models learned as density estimators can be exploited in representation learning beside being toolboxes used to answer inference queries only.
Visualizing and Understanding Sum-Product Networks
Sum-Product Networks (SPNs) are recently introduced deep tractable probabilistic models by which several kinds of inference queries can be answered exactly and in a tractable time.
Sum-Product Networks for Hybrid Domains
While all kinds of mixed data -from personal data, over panel and scientific data, to public and commercial data- are collected and stored, building probabilistic graphical models for these hybrid domains becomes more difficult.
Probabilistic Deep Learning using Random Sum-Product Networks
The need for consistent treatment of uncertainty has recently triggered increased interest in probabilistic deep learning methods.
Automatic Bayesian Density Analysis
Classical approaches for {exploratory data analysis} are usually not flexible enough to deal with the uncertainty inherent to real-world data: they are often restricted to fixed latent interaction models and homogeneous likelihoods; they are sensitive to missing, corrupt and anomalous data; moreover, their expressiveness generally comes at the price of intractable inference.
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 interface to inference, learning and manipulation routines for deep and tractable probabilistic models called Sum-Product Networks (SPNs).
From Variational to Deterministic Autoencoders
Variational Autoencoders (VAEs) provide a theoretically-backed and popular framework for deep generative models.
Conditional Sum-Product Networks: Imposing Structure on Deep Probabilistic Architectures
In contrast, deep probabilistic models such as sum-product networks (SPNs) capture joint distributions in a tractable fashion, but still lack the expressive power of intractable models based on deep neural networks.
Hybrid Probabilistic Inference with Logical Constraints: Tractability and Message Passing
Weighted model integration (WMI) is a very appealing framework for probabilistic inference: it allows to express the complex dependencies of real-world hybrid scenarios where variables are heterogeneous in nature (both continuous and discrete) via the language of Satisfiability Modulo Theories (SMT); as well as computing probabilistic queries with arbitrarily complex logical constraints.
On Tractable Computation of Expected Predictions
In this paper, we identify a pair of generative and discriminative models that enables tractable computation of expectations, as well as moments of any order, of the latter with respect to the former in case of regression.
Scaling up Hybrid Probabilistic Inference with Logical and Arithmetic Constraints via Message Passing
Weighted model integration (WMI) is a very appealing framework for probabilistic inference: it allows to express the complex dependencies of real-world problems where variables are both continuous and discrete, via the language of Satisfiability Modulo Theories (SMT), as well as to compute probabilistic queries with complex logical and arithmetic constraints.
Einsum Networks: Fast and Scalable Learning of Tractable Probabilistic Circuits
Probabilistic circuits (PCs) are a promising avenue for probabilistic modeling, as they permit a wide range of exact and efficient inference routines.
Handling Missing Data in Decision Trees: A Probabilistic Approach
As such, handling missing data in decision trees is a well studied problem.
Strudel: Learning Structured-Decomposable Probabilistic Circuits
Probabilistic circuits (PCs) represent a probability distribution as a computational graph.
Imagining Grounded Conceptual Representations from Perceptual Information in Situated Guessing Games
However, as shown by Suglia et al. (2020), existing models fail to learn truly multi-modal representations, relying instead on gold category labels for objects in the scene both at training and inference time.
Probabilistic Inference with Algebraic Constraints: Theoretical Limits and Practical Approximations
Weighted model integration (WMI) is a framework to perform advanced probabilistic inference on hybrid domains, i. e., on distributions over mixed continuous-discrete random variables and in presence of complex logical and arithmetic constraints.
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 interacting" paradigm.
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 reason about tractable generative and discriminative models.
Tractable Computation of Expected Kernels
Computing the expectation of kernel functions is a ubiquitous task in machine learning, with applications from classical support vector machines to exploiting kernel embeddings of distributions in probabilistic modeling, statistical inference, causal discovery, and deep learning.
A Compositional Atlas of Tractable Circuit Operations for Probabilistic Inference
Circuit representations are becoming the lingua franca to express and reason about tractable generative and discriminative models.
Neural Concept Formation in Knowledge Graphs
In this work, we investigate how to learn novel concepts in Knowledge Graphs (KGs) in a principled way, and how to effectively exploit them to produce more accurate neural link prediction models.
Efficient and Reliable Probabilistic Interactive Learning with Structured Outputs
In this position paper, we study interactive learning for structured output spaces, with a focus on active learning, in which labels are unknown and must be acquired, and on skeptical learning, in which the labels are noisy and may need relabeling.
Semantic Probabilistic Layers for Neuro-Symbolic Learning
We design a predictive layer for structured-output prediction (SOP) that can be plugged into any neural network guaranteeing its predictions are consistent with a set of predefined symbolic constraints.
ChemAlgebra: Algebraic Reasoning on Chemical Reactions
Measuring the robustness of reasoning in machine learning models is challenging as one needs to provide a task that cannot be easily shortcut by exploiting spurious statistical correlations in the data, while operating on complex objects and constraints.
From MNIST to ImageNet and Back: Benchmarking Continual Curriculum Learning
Continual learning (CL) is one of the most promising trends in recent machine learning research.
How to Turn Your Knowledge Graph Embeddings into Generative Models
Some of the most successful knowledge graph embedding (KGE) models for link prediction -- CP, RESCAL, TuckER, ComplEx -- can be interpreted as energy-based models.
Ranked #3 on
Link Property Prediction
on ogbl-biokg
Knowledge Graph Embeddings in the Biomedical Domain: Are They Useful? A Look at Link Prediction, Rule Learning, and Downstream Polypharmacy Tasks
We achieve a three-fold improvement in terms of performance based on the HITS@10 score over previous work on the same biomedical knowledge graph.
Not All Neuro-Symbolic Concepts Are Created Equal: Analysis and Mitigation of Reasoning Shortcuts
Neuro-Symbolic (NeSy) predictive models hold the promise of improved compliance with given constraints, systematic generalization, and interpretability, as they allow to infer labels that are consistent with some prior knowledge by reasoning over high-level concepts extracted from sub-symbolic inputs.
Subtractive Mixture Models via Squaring: Representation and Learning
Mixture models are traditionally represented and learned by adding several distributions as components.
Taming the Sigmoid Bottleneck: Provably Argmaxable Sparse Multi-Label Classification
We then show that they can be prevented in practice by introducing a Discrete Fourier Transform (DFT) output layer, which guarantees that all sparse label combinations with up to $k$ active labels are argmaxable.
Probabilistic Integral Circuits
In contrast, probabilistic circuits (PCs) are hierarchical discrete mixtures represented as computational graphs composed of input, sum and product units.
Wearable data from subjects playing Super Mario, sitting university exams, or performing physical exercise help detect acute mood episodes via self-supervised learning
In this paper, we overcome this data bottleneck and advance the detection of MDs acute episode vs stable state from wearables data on the back of recent advances in self-supervised learning (SSL).
PIXAR: Auto-Regressive Language Modeling in Pixel Space
However, these pixel-based LLMs are limited to discriminative tasks (e. g., classification) and, similar to BERT, cannot be used to generate text.
BEARS Make Neuro-Symbolic Models Aware of their Reasoning Shortcuts
Neuro-Symbolic (NeSy) predictors that conform to symbolic knowledge - encoding, e. g., safety constraints - can be affected by Reasoning Shortcuts (RSs): They learn concepts consistent with the symbolic knowledge by exploiting unintended semantics.
On the Independence Assumption in Neurosymbolic Learning
Many such systems assume that the probabilities of the considered symbols are conditionally independent given the input to simplify learning and reasoning.
