
Every Model Learned by Gradient Descent Is Approximately a Kernel Machine
Deep learning's successes are often attributed to its ability to automat...
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Amodal 3D Reconstruction for Robotic Manipulation via Stability and Connectivity
Learningbased 3D object reconstruction enables single or fewshot esti...
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SelfSupervised ObjectLevel Deep Reinforcement Learning
Current deep reinforcement learning approaches incorporate minimal prior...
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NeuralSymbolic Learning and Reasoning: A Survey and Interpretation
The study and understanding of human behaviour is relevant to computer s...
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Deep Learning as a Mixed ConvexCombinatorial Optimization Problem
As neural networks grow deeper and wider, learning networks with hardth...
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The SumProduct Theorem: A Foundation for Learning Tractable Models
Inference in expressive probabilistic models is generally intractable, w...
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Recursive Decomposition for Nonconvex Optimization
Continuous optimization is an important problem in many areas of AI, inc...
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On the Latent Variable Interpretation in SumProduct Networks
One of the central themes in SumProduct networks (SPNs) is the interpre...
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Exchangeable Variable Models
A sequence of random variables is exchangeable if its joint distribution...
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Structured Message Passing
In this paper, we present structured message passing (SMP), a unifying f...
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Learning Arithmetic Circuits
Graphical models are usually learned without regard to the cost of doing...
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FormulaBased Probabilistic Inference
Computing the probability of a formula given the probabilities or weight...
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SumProduct Networks: A New Deep Architecture
The key limiting factor in graphical model inference and learning is the...
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Probabilistic Theorem Proving
Many representation schemes combining firstorder logic and probability ...
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Approximation by Quantization
Inference in graphical models consists of repeatedly multiplying and sum...
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Pedro Domingos
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Professor of computer science at UW and author of 'The Master Algorithm'