Summary of An Attempt to Generate New Bridge Types From Latent Space Of Energy-based Model, by Hongjun Zhang
An attempt to generate new bridge types from latent space of energy-based model
by Hongjun Zhang
First submitted to arxiv on: 31 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes an energy-based model for generative innovation in bridge design. The loss function is grounded in game theory, making it clear and simple to understand. This approach eliminates the need for maximum likelihood estimation and Monte Carlo methods. A neural network represents the energy function, assuming a Boltzmann distribution of bridge populations. Langevin dynamics are used to generate new samples with low energy values, establishing a generative model for bridge design. The model is trained on a dataset of symmetric structured images of different bridge types, enabling accurate calculation of real and fake sample energies. By sampling from the latent space and applying gradient descent, the model generates new bridge types that differ from the training data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a special kind of computer program that helps design new bridges. It’s like a game where the computer tries to find better solutions. The program is based on a mathematical idea called “energy” and uses a type of machine learning called neural networks. The program looks at pictures of different types of bridges and tries to make new ones that are similar but also unique. However, the training process can be slow and unstable, making it hard to get good results. |
Keywords
* Artificial intelligence * Energy based model * Generative model * Gradient descent * Latent space * Likelihood * Loss function * Machine learning * Neural network
