Ibm data generator series#

It has a one-to-one mapping to the probability chain rule.

In Figure 1, the GPT model training uses autoregressive loss. The GPT autoregressive loss directly models the data joint probability distribution shown in Figure 1.

Ibm data generator series#

As you may know, any joint probability distribution can be factored into the product of a series of conditional probability distributions according to the probability chain rule. The GPT model turns out to be a superior generative model. GPT3 has been widely used across multiple industries and domains, from productivity and education to creativity and games. OpenAI’s GPT3 uses the decoder part of the transformer model and has 175B parameters. For example, the NVIDIA Megatron-Turing NLG model obtains excellent results with 530B parameters. The self-attention encoding and decoding architecture of the transformer model has proven to be accurate in modeling data distribution and is scalable to larger datasets. Transformer models have recently achieved great success in the natural language processing (NLP) domain. The VAE model has difficulty generating sharp data points due to non-autoregressive loss.The mode collapse problem in the GAN model causes the generated data to miss some modes in the training data distribution.However, there are known issues with VAE and GAN models for synthetic data generation: Two popular generative models have achieved some success in the past: Variational Auto-Encoder ( VAE) and Generative Adversarial Network ( GAN). Transformers: the better synthetic data generatorĭeep learning generative models are a natural fit to model complicated real-world data. We explain how synthetically generated data can be used as a valid substitute for real-life data in machine learning algorithms to protect user privacy while making accurate predictions. In this post, we explain how synthetic data can be artificially produced with transformer models, using NVIDIA NeMo as an example. Successful synthetic data generation involves capturing the distribution while maintaining privacy and conditionally generating new data, which can then be used to make more robust models or used for time-series forecasting. Generating synthetic data also provides a degree of data privacy without compromising quality or realism. This allows for data to be shared across teams or externally. Continuous sampling can generate an infinite number of data points including labels. Synthetically generated data is a potential solution to address these challenges because it generates data points by sampling from the model. However, data poses many challenges for enterprises: difficulty in data labeling, ineffective data governance, limited data availability, data privacy, and so on. Big data, new algorithms, and fast computation are three main factors that make the modern AI revolution possible.