Related Experiment Video
Updated: Jul 28, 2025
08:05
Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
Published on: June 30, 2020
7.6K
A survey of sum-product networks structural learning
Riting Xia1, Yan Zhang2, Xueyan Liu3
1Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China; College of Artificial Intelligence, Jilin University, Changchun, Jilin, 130012, China.
Summary
This paper surveys sum-product networks (SPNs), a type of deep probabilistic model. It reviews SPN structure learning algorithms, discussing their motivations, theories, categorizations, and evaluations.
Area of Science:
- Artificial Intelligence
- Machine Learning
- Deep Learning
Background:
- Sum-product networks (SPNs) are advanced deep probabilistic models offering a balance between tractability and expressive efficiency.
- SPNs demonstrate greater interpretability compared to standard deep neural models.
- The performance and complexity of SPNs are intrinsically linked to their structural design.
Approach:
- This paper presents a comprehensive review of sum-product network (SPN) structure learning.
- It systematically categorizes various SPN structure learning algorithms.
- The review covers the motivations behind SPN structure learning, relevant theories, evaluation methodologies, and online resources.
Key Points:
- SPN structure learning is crucial for optimizing expressiveness and complexity.
- The survey provides a structured overview of existing SPN structure learning techniques.
- It highlights the importance of effective algorithm design for SPN applications.
Conclusions:
- This work offers the first dedicated survey on SPN structure learning.
- It aims to serve as a valuable reference for researchers in artificial intelligence and machine learning.
- Future research directions and open issues in SPN structure learning are also discussed.
Related Concept Videos
01:22Sum and Difference OpAmps
811
Operational amplifiers (op-amps) are versatile devices that extend beyond amplification. In this context, two specific op-amp configurations are explored: the summing and difference amplifiers.
A summing amplifier, or an adder, utilizes an op-amp to merge multiple input signals into a single output signal. When audio signals are introduced into its input channels, the input resistors initiate currents that traverse feedback resistors, resulting in an output voltage. Applying Kirchhoff's...
A summing amplifier, or an adder, utilizes an op-amp to merge multiple input signals into a single output signal. When audio signals are introduced into its input channels, the input resistors initiate currents that traverse feedback resistors, resulting in an output voltage. Applying Kirchhoff's...
811
01:27Associative Learning
465
Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
Classical conditioning, also known...
465
01:12Observational Learning
246
Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
246
01:24Sequence Networks of Rotating Machines
127
A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
127
01:19Graded Potential
4.1K
Graded potentials are localized fluctuations in the cell membrane's electrical charge, commonly found in the dendrites of neurons. The magnitude of these potential changes depends on the strength of the initiating stimulus. In a membrane at its resting potential, a graded potential signifies a voltage shift either above -70 mV or below -70 mV.
Graded potentials fall into two categories: depolarizing and hyperpolarizing. Depolarizing graded potentials typically occur when sodium (Na+) or...
Graded potentials fall into two categories: depolarizing and hyperpolarizing. Depolarizing graded potentials typically occur when sodium (Na+) or...
4.1K
01:16SFG Algebra
146
In Signal Flow Graph (SFG) algebra, the value a node represents is determined by the sum of all signals entering that node. This summed value is then transmitted through every branch leaving the node, making the SFG a powerful tool for visualizing and analyzing control systems.
Each node in an SFG corresponds to a variable, and the interactions between nodes are represented by branches with associated gains. When multiple branches lead into a node, the value at that node is the sum of the...
Each node in an SFG corresponds to a variable, and the interactions between nodes are represented by branches with associated gains. When multiple branches lead into a node, the value at that node is the sum of the...
146