/explore
|
You are here 
|
evjang.com | ||
| | | | |
blog.evjang.com
|
|
| | | | | The 30th annual Neural Information Processing Systems (NIPS) conference took place in Barcelona in early December. In this post, I share my ... | |
| | | | |
yang-song.net
|
|
| | | | | This blog post focuses on a promising new direction for generative modeling. We can learn score functions (gradients of log probability density functions) on a large number of noise-perturbed data distributions, then generate samples with Langevin-type sampling. The resulting generative models, often called score-based generative models, has several important advantages over existing model families: GAN-level sample quality without adversarial training, flexible model architectures, exact log-likelihood ... | |
| | | | |
amatria.in
|
|
| | | | | [AI summary] The provided text is an extensive overview of various large language models (LLMs) and their architectures, training tasks, and applications. It includes detailed descriptions of models like GPT, T5, BERT, and others, along with their pre-training objectives, parameter counts, and specific use cases. The text also references key research papers, surveys, and resources for further reading on LLMs and related topics. | |
| | | | |
angusturner.github.io
|
|
| | | Machine Learning and Data Science. | ||
