I lead an LLM pre-training team at Yandex and optimise large-scale distributed training runs. I lead an LLM pre-training team at Yandex and optimise large-scale distributed training runs. I lead an ...

Abstract: Bayesian inference provides a methodology for parameter estimation and uncertainty quantification in machine learning and deep learning methods. Variational inference and Markov Chain ...

Learn how Network in Network (NiN) architectures work and how to implement them using PyTorch. This tutorial covers the concept, benefits, and step-by-step coding examples to help you build better ...

Abstract: Pytorch_EHR is a codebase enabling fast prototyping of deep learning-based predictive models using electronic health records structured data. Rather than a collection of vertical pipelines ...

In the ever-evolving large language models (LLMs), a persistent challenge has been the need for more standardization, hindering effective model comparisons and impeding the need for reevaluation. The ...

Most of the candle models have been ported from the Python transformers library (you can often find in the comments pointers to the source for the Python implementation so that it's easy to look at ...

In areas with clearly defined reward functions, like games, reinforcement learning (RL) has outperformed human performance. Unfortunately, it is difficult or impossible for many tasks in the real ...

Note, the instructions below assume you are using a Linux environment. Run the following in the Azure Cloud Shell to create a sample function app with a Python runtime: #!/bin/bash # Function app and ...