376 citations · 1.1k across the 17 of their papers we have counts for
18 papers
Every Model Learned by Gradient Descent Is Approximately a Kernel Machine
Pedro Domingos
Deep learning's successes are often attributed to its ability to automatically discover new representations of the data, rather than relying on handcrafted features like other lear…
Amodal 3D Reconstruction for Robotic Manipulation via Stability and Connectivity
William Agnew, Christopher Xie, Aaron Walsman +4
Learning-based 3D object reconstruction enables single- or few-shot estimation of 3D object models. For robotics, this holds the potential to allow model-based methods to rapidly a…
Relevance-Guided Modeling of Object Dynamics for Reinforcement Learning
William Agnew, Pedro Domingos
Current deep reinforcement learning (RL) approaches incorporate minimal prior knowledge about the environment, limiting computational and sample efficiency. \textit{Objects} provid…
Neural-Symbolic Learning and Reasoning: A Survey and Interpretation
Tarek R. Besold, Artur d'Avila Garcez, Sebastian Bader +11
The study and understanding of human behaviour is relevant to computer science, artificial intelligence, neural computation, cognitive science, philosophy, psychology, and several…
Deep Learning as a Mixed Convex-Combinatorial Optimization Problem
Abram L. Friesen, Pedro Domingos
As neural networks grow deeper and wider, learning networks with hard-threshold activations is becoming increasingly important, both for network quantization, which can drastically…
The Sum-Product Theorem: A Foundation for Learning Tractable Models
Abram L. Friesen, Pedro Domingos
Inference in expressive probabilistic models is generally intractable, which makes them difficult to learn and limits their applicability. Sum-product networks are a class of deep…
Recursive Decomposition for Nonconvex Optimization
Abram L. Friesen, Pedro Domingos
Continuous optimization is an important problem in many areas of AI, including vision, robotics, probabilistic inference, and machine learning. Unfortunately, most real-world optim…
On the Latent Variable Interpretation in Sum-Product Networks
Robert Peharz, Robert Gens, Franz Pernkopf +1
One of the central themes in Sum-Product networks (SPNs) is the interpretation of sum nodes as marginalized latent variables (LVs). This interpretation yields an increased syntacti…
Learning Tractable Probabilistic Models for Fault Localization
Aniruddh Nath, Pedro Domingos
In recent years, several probabilistic techniques have been applied to various debugging problems. However, most existing probabilistic debugging systems use relatively simple stat…
Exchangeable Variable Models
Mathias Niepert, Pedro Domingos
A sequence of random variables is exchangeable if its joint distribution is invariant under variable permutations. We introduce exchangeable variable models (EVMs) as a novel class…
Structured Message Passing
Vibhav Gogate, Pedro Domingos
In this paper, we present structured message passing (SMP), a unifying framework for approximate inference algorithms that take advantage of structured representations such as alge…
Markov Logic in Infinite Domains
Parag Singla, Pedro Domingos
Combining first-order logic and probability has long been a goal of AI. Markov logic (Richardson & Domingos, 2006) accomplishes this by attaching weights to first-order formulas an…
Learning Arithmetic Circuits
Daniel Lowd, Pedro Domingos
Graphical models are usually learned without regard to the cost of doing inference with them. As a result, even if a good model is learned, it may perform poorly at prediction, bec…
Formula-Based Probabilistic Inference
Vibhav Gogate, Pedro Domingos
Computing the probability of a formula given the probabilities or weights associated with other formulas is a natural extension of logical inference to the probabilistic setting. S…
Sum-Product Networks: A New Deep Architecture
Hoifung Poon, Pedro Domingos
The key limiting factor in graphical model inference and learning is the complexity of the partition function. We thus ask the question: what are general conditions under which the…
Probabilistic Theorem Proving
Vibhav Gogate, Pedro Domingos
Many representation schemes combining first-order logic and probability have been proposed in recent years. Progress in unifying logical and probabilistic inference has been slower…
Approximation by Quantization
Vibhav Gogate, Pedro Domingos
Inference in graphical models consists of repeatedly multiplying and summing out potentials. It is generally intractable because the derived potentials obtained in this way can be…
Relational Dynamic Bayesian Networks
P. Domingos, S. Sanghai, D. Weld
Stochastic processes that involve the creation of objects and relations over time are widespread, but relatively poorly studied. For example, accurate fault diagnosis in factory as…