4 ms·
A tangent but if you're frustrated with the dismissal of math in ML I think you would probably enjoy diving in the Reinforcement Learning subfield; although som
by NalNezumi 2y ago
A tangent but if you're frustrated with the dismissal of math in ML I think you would probably enjoy diving in the Reinforcement Learning subfield; although some people tend to call it experimental math ;]
ML applied to robotics/embodiment is another fun topic, where physics is very relevant in every step of it. A bit harder to dataset-hack when a physical system is crumbling in front of you.
- godelski 2y agoOh I've found some areas. I'm particularly fond of explicit density models and normalizing flows. I do like RL too and was very surprised to hear that the math was "confusing" when it just seemed like weird notation. But it's hard to get works through review because even if you get SOTA for your architecture some asshat will ask you why you aren't better than the architectures being worked on by tens of thousands of people and with compute budgets 1000x yours. Though it's better if you find a very small niche so the reviews you get are more likely to actually know the bare basics of the topic.