What would be the best way to create a new generative model using novel techniques?
Creating a new generative model using novel techniques requires a lot of research, experimentation, and creativity. A good approach is to identify a specific problem or area of interest that has not yet been explored or solved with existing models. Then, you can explore different algorithms, architectures, and training techniques to develop a new model that can effectively tackle the problem. It's important to stay up-to-date with the latest research in the field and to constantly iterate and improve upon your model. Collaborating with other researchers and seeking feedback from peers can also help to refine and optimize your approach.
I'd more looking for a detailed technical answer. Can you provide me with a list of cutting edge model architectures, techniques, etc. that I can explore? Give a brief summary of each.
Of course! Here are some cutting edge model architectures and techniques that you may find useful for creating a new generative model: 

1. Generative Adversarial Networks (GANs): GANs are a class of deep neural networks that can generate realistic images and data by training a generator network to produce data that is similar to the real data, while training a discriminator network to distinguish between the real and generated data. 
2. Variational Autoencoders (VAEs): VAEs are another class of generative models that learn to encode data into a low-dimensional latent space and decode it back to the original data. They can be used to generate new data that is similar to the training data. 
3. Transformer Models: Transformer models are a type of neural network architecture that have revolutionized natural language processing tasks such as machine translation, language modeling, and text generation. They use self-attention mechanisms to process input data, and have achieved state-of-the-art results on a variety of language tasks. 
4. Reinforcement Learning: Reinforcement learning is a subfield of machine learning that focuses on training agents to make optimal decisions in dynamic environments. It has been used to develop generative models that can learn to produce novel and creative outputs. 
5. Neural Ordinary Differential Equations (ODEs): ODEs are a mathematical framework that can be used to model dynamical systems. Neural ODEs are a type of neural network that learns to represent the evolution of a system over time, and can be used for tasks such as image generation and data imputation. 
6. Flow-Based Models: Flow-based models are a type of generative model that transform a simple base distribution into a complex distribution that matches the training data. They have been used for tasks such as image synthesis and density estimation.

These are just a few examples of cutting-edge model architectures and techniques that you can explore. It's important to keep in mind that the choice of technique will depend on the specific problem you're trying to solve and the type of data you're working with.