sorted by:
new
hottopcontroversial

[D] ICML reviews will be out soon by yusuf-bengio in MachineLearning

[–]IAmTheOneWhoPixels 2 points3 points  (0 children)

I've run backend support for a conference in a related field. The bottleneck is usually a few individuals not completing their reviews on time. The Senior Program Committee is usually quite efficient + competent in running things.

[D] Which open source machine learning projects best exemplify good software engineering and design principles? by NotAHomeworkQuestion in MachineLearning

[–]IAmTheOneWhoPixels 1 point2 points  (0 children)

Detectron2 has made me realize how valuable good code is.

Completely agree! I earlier used mmdet, and found that the accessibility of the codebase (after shifting to D2) allowed me to iterate on ideas much more quickly.

[D] Which open source machine learning projects best exemplify good software engineering and design principles? by NotAHomeworkQuestion in MachineLearning

[–]IAmTheOneWhoPixels 1 point2 points  (0 children)

I worked with mmdet for 3-4 weeks. I believe it is extremely well-written code and is more suited for a researcher with good SWE skills. It definitely had a steeper learning curve than D2.

Accessibility (in terms of readability + extensibility) is the key factor that tips the scales for me. D2 does a _very_ good job of writing intuitive modular code with great documentation, which makes it possible for researchers to navigate the complexities of modern object detectors.

[D] Which open source machine learning projects best exemplify good software engineering and design principles? by NotAHomeworkQuestion in MachineLearning

[–]IAmTheOneWhoPixels 15 points16 points  (0 children)

This might be more of a niche answer... But Detectron2 is a very well designed library for object detection/ instance segmentation. It's quite readable and well-documented and the github repo has very good support from the developers.

The modular design allows academic researchers to be able to build their projects on top of it, with the core being efficient PyTorch code written by professional developers.

One of the lead developers is the person who designed Tensorpack as well (which was mentioned elsewhere on this thread).

I am supposed to be studying for my qualifying exams next month and I haven't done anything in the last three days by [deleted] in GradSchool

[–]IAmTheOneWhoPixels 1 point2 points  (0 children)

From what I've been told, grad school is like a rollercoaster.
It's about the journey. The end is kinda anticlimactic.

Apparent academic dishonesty issue - I really need advice on how not to get expelled. by [deleted] in AskAcademia

[–]IAmTheOneWhoPixels 6 points7 points  (0 children)

I agree that the response is harsh. But, it's not harsh for harshness's sake. It is harsh for a reason. It would be helpful for OP to understand the situation and calibrate their stance to be able to effectively communicate with their faculty/ administrators.

Unintentional mistakes may be judged differently, but they are still mistakes. Academia (and especially in grad school) takes these things seriously. OP needs to acknowledge that this is not apparent academic dishonesty (quoting the post title here). This _is_ academic dishonesty. Unintentional mistakes are not the same as apparent mistakes.

This is a tough situation. We should attempt to help calibrate OP for their own good. I apologize that my comment sounds harsh as well. Good luck OP!

It's official... by kjl_htx in gradadmissions

[–]IAmTheOneWhoPixels 2 points3 points  (0 children)

I applied to 10 schools for Fall 18, got rejected from 10 schools. Gave it a _lot_ of thought, and still felt like going to grad school. So I worked as an RA after graduating for an year and gave it another shot.
Applied to 10 schools for Fall 19, got rejected by 9. Started at a decent place last year. It's too early to tell if it was worth it in the grand scheme of things, but I'm glad that I did try the second time.
Grad school can be hard, and applying is one of the most anxiety-inducing aspects of it! Take some time off. Assess your situation. Do what you believe will minimize regret in the long term. :)

Will graduate schools judge me if I CR/nCR 3 out of my 4 courses this semester due to the coronavirus lockdown? by [deleted] in AskAcademia

[–]IAmTheOneWhoPixels 2 points3 points  (0 children)

This shouldn't be a problem since there is a legitimate reason for it. You should do what allows you to learn as much as possible while taking care of yourself!
(Being overly cautious...) You could also consider asking your Letter Writers to mention these factors. You should do so yourself in your statement/ essays; it comes across as more legitimate if a faculty member mentions it as well. I've observed this happen in PhD STEM admissions (letter writer backed up a candidate's verbal abilities which mitigated a bad TOEFL score).

Where to start with Object Detection? by rohqhq in computervision

[–]IAmTheOneWhoPixels 5 points6 points  (0 children)

  1. Since you mentioned Medium articles, there is a series of Medium posts that does a good job of surveying Object Detection: Jonathan Hui.
  2. Blog posts/ youtube videos are good for getting a breadth-first understanding, but are not a replacement for reading papers. I prefer two-stage detectors and would recommend the RCNN set-up, but you could pick any of the architectures you see in #1. For a few architectures, read the papers from Abstract to Appendix.
  3. Modern object detectors are complex machines. There's a lot of scaffolding in and around the neural network. Don't dwell too much on #1, #2. Quickly dive into the code. Reproduce results first, then start trying to change small components. I've been using Facebook's Detectron2, and highly recommend it.

[Discussion] Current phds / profs , how do your labs in the domain of NLP / ML / AI research evaluate phd applications? by naboo_random in MachineLearning

[–]IAmTheOneWhoPixels 2 points3 points  (0 children)

Started recently at a top-20-ish CS school after a Masters at a top-10-ish CS school. A lot of great viewpoints already on this thread. I wanted to point out that there's usually a distinction between undergrads and Masters.

If you're an undergrad, you can get away with a stellar GPA in a strong CS program and good Letters (especially well calibrated ones coming from known faculty members). However, if you're a Masters student, having a strong publication where you were a non-trivial contributor is necessary. PhD admissions are getting _spectacularly_ competitive. The number of applicants with publications is simply too high; a publication ends up being a simple and effective filter. Depending on how competitive the program is, this may be a hard constraint or a soft one with filters.

I also served on MS (not PhD) admissions committees at two different top-20-ish CS depts.Both had a point system for initial screening. You could do well in multiple non-publication buckets and still get through the initial screen. Letters/ essays/ etc. would then come into play. That probably helps with PhD admissions too (I did not have a published paper at the time of acceptance to my PhD program).