Posts

Post accepted to the ICLR 2022 Blog Track. In this post, we provide an in-depth overview of methods outlined in the paper “Learning Neural Generative Dynamics for Molecular Conformation Generation,” discuss the impact of the work in the context of other conformation generation approaches, and additionally discuss future potential applications to improve the diversity and stability of generated conformations.

We interact with machine learning algorithms every day–from scrolling through social media, to navigating around town, to selecting word recommendations when we text. But what do we know about these machine learning methods?

Richard Feynman once said “What I cannot create I do not understand.” Therefore, to truly understand the human visual system, we must learn to create it. One of the most effective forms of such creation is the Convolutional Neural Network (CNN) system to mimic the human visual system.

10% of the world’s population lives in extreme poverty. This means that around 700 million people are living on less than $1.90 a day (1). And while $1.90 is the standard set by the World Bank defining the international poverty line (2), by no means does it imply that a person earning more than this amount has anywhere near suitable standards of living.

Employing forest guards to protect endangered species. Organising volunteers for beach cleanups. Encouraging people to lead greener lifestyles. These are just some of the traditional methods that have been used to protect our environment.

If you’re involved in research, you’re probably going to give a reading group presentation at some point. Many professors push their PhD students to give talks. Giving these talks helps researchers build the ability to read and understand papers quickly, and the ability to communicate findings effectively.

Say you are a researcher in autonomous vehicles, and as part of a project for a funder, you want to make an algorithm to automatically tell if a frame of video has a sidewalk or not.