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11/30/2020
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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09/28/2020
Amodal 3D Reconstruction for Robotic Manipulation via Stability and Connectivity
Learning-based 3D object reconstruction enables single- or few-shot esti...
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03/03/2020
Self-Supervised Object-Level Deep Reinforcement Learning
Current deep reinforcement learning approaches incorporate minimal prior...
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11/10/2017
Neural-Symbolic Learning and Reasoning: A Survey and Interpretation
The study and understanding of human behaviour is relevant to computer s...
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10/31/2017
Deep Learning as a Mixed Convex-Combinatorial Optimization Problem
As neural networks grow deeper and wider, learning networks with hard-th...
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11/11/2016
The Sum-Product Theorem: A Foundation for Learning Tractable Models
Inference in expressive probabilistic models is generally intractable, w...
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11/08/2016
Recursive Decomposition for Nonconvex Optimization
Continuous optimization is an important problem in many areas of AI, inc...
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01/22/2016
On the Latent Variable Interpretation in Sum-Product Networks
One of the central themes in Sum-Product networks (SPNs) is the interpre...
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05/02/2014
Exchangeable Variable Models
A sequence of random variables is exchangeable if its joint distribution...
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09/26/2013
Structured Message Passing
In this paper, we present structured message passing (SMP), a unifying f...
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06/13/2012
Learning Arithmetic Circuits
Graphical models are usually learned without regard to the cost of doing...
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03/15/2012
Formula-Based Probabilistic Inference
Computing the probability of a formula given the probabilities or weight...
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02/14/2012
Sum-Product Networks: A New Deep Architecture
The key limiting factor in graphical model inference and learning is the...
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02/14/2012
Probabilistic Theorem Proving
Many representation schemes combining first-order logic and probability ...
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02/14/2012