Hi! I’m a second year PhD student in machine learning and I was looking for an Internship (possibly) in Europe to apply for next year, 4 months ideally.

My lab does mainly theoretical work and started doing a bit of deep learning just 3 years ago. When I started i was more focused on the experiments (deep learning) and now i am shifting towards the theoretical side due to the lab and supervisor expertise. Since i like both aspects of machine learning I though that an internship could be a good opportunity to make more impactful experimental work and also make some connections.

I only have 2 (ICML/Neurips) publication as a co-author. Do you think something like Deepmind in London could already be out of reach for me? Do you know about some nice internships program that could suit me better?

Let’s say we have two (or more) embedding spaces learned from different data spaces:

There is one one global task T that all embedding spaces are evaluated on.

To perform better on T than each embedding space would on their own it follows that we can just concatenate each vector of each embedding space. But is there a better method than to simply concatenate?

Hi, I’m doing some research and playing with the waymo dataset. To start I’m doing simple 2d object detection on their subset of data with 2d bounding boxes. Has anyone done similar? I am having trouble setting an expectation of accuracy.

Furthermore the dataset seems to be chunked into individual drive segments, where a lot of the images are temporally ‘close’ meaning the same cars are in the frame. I believe this is causing early overfitting. Wanted to see if anyone else is experiencing this.

Thanks!

https://teddykoker.com/2019/12/beating-the-odds-machine-learning-for-horse-racing/

For a recent project, I set out to see if I could use machine learning to identify inefficiencies in horse racing wagering. It was interesting to find how such methods can work, even without much in-domain knowledge.